diff --git a/tests/layer_tests/ovc_python_api_tests/test_paddle.py b/tests/layer_tests/ovc_python_api_tests/test_paddle.py
index a91e3f9e723..80b8efefea3 100644
--- a/tests/layer_tests/ovc_python_api_tests/test_paddle.py
+++ b/tests/layer_tests/ovc_python_api_tests/test_paddle.py
@@ -101,3 +101,35 @@ class TestMoConvertPaddle(CommonMOConvertTest):
         if mo_params is not None:
             test_params.update(mo_params)
         self._test_by_ref_graph(temp_dir, test_params, graph_ref, compare_tensor_names=False)
+
+
+class TestPaddleConversionParams(CommonMOConvertTest):
+    paddle_is_imported = False
+    try:
+        import paddle
+        paddle_is_imported = True
+    except ImportError:
+        pass
+
+    test_data = [
+        {'params_test': {'input': paddle.shape(paddle.to_tensor(np.random.rand(2, 3, 4)))},
+         'fw_model': make_pd_hapi_graph_model([1, 2]),
+         'ref_model': make_ref_graph_model([2, 3, 4])},
+        {'params_test': {'input': paddle.to_tensor(np.random.rand(5, 6)).shape},
+         'fw_model': make_pd_hapi_graph_model([1, 2, 3]),
+         'ref_model': make_ref_graph_model([5, 6])},
+        {'params_test': {'input': (paddle.to_tensor(np.random.rand(4, 2, 7)).shape, paddle.int32)},
+         'fw_model': make_pd_hapi_graph_model([2, 3]),
+         'ref_model': make_ref_graph_model([4, 2, 7], np.int32)},
+    ] if paddle_is_imported else []
+
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.nightly
+    def test_conversion_params(self, params, ie_device, precision, ir_version,
+                                 temp_dir, use_new_frontend, use_old_api):
+        fw_model = params['fw_model']
+        test_params = params['params_test']
+        ref_model = params['ref_model']
+
+        test_params.update({'input_model': fw_model})
+        self._test_by_ref_graph(temp_dir, test_params, ref_model, compare_tensor_names=False)
diff --git a/tests/layer_tests/ovc_python_api_tests/test_pytorch.py b/tests/layer_tests/ovc_python_api_tests/test_pytorch.py
index 52d66690459..5ae2dcac31c 100644
--- a/tests/layer_tests/ovc_python_api_tests/test_pytorch.py
+++ b/tests/layer_tests/ovc_python_api_tests/test_pytorch.py
@@ -1145,3 +1145,75 @@ class ConvertRaises(unittest.TestCase):
         with self.assertRaisesRegex(Exception, ".*Cannot recognize input model.*"):
             with tempfile.NamedTemporaryFile() as tmpfile:
                 convert_model(tmpfile.name)
+
+
+def create_model_three_inputs():
+    from torch import nn
+
+    class NeuralNetwork(nn.Module):
+        def __init__(self):
+            super(NeuralNetwork, self).__init__()
+            self.linear_relu_stack = nn.Sequential(
+                nn.ReLU(),
+                nn.Sigmoid(),
+            )
+
+        def forward(self, x, y, z):
+            out = self.linear_relu_stack(x + y + z),
+            return out
+    return NeuralNetwork()
+
+
+def make_ref_model_three_inputs(shape, dtype=np.float32):
+    x = ov.opset8.parameter(PartialShape(
+        shape), name="x", dtype=dtype)
+    y = ov.opset8.parameter(PartialShape(
+        shape), name="y", dtype=dtype)
+    z = ov.opset8.parameter(PartialShape(
+        shape), name="z", dtype=dtype)
+    add1 = ov.opset8.add(x, y)
+    add2 = ov.opset8.add(add1, z)
+
+    relu = ov.opset8.relu(add2)
+
+    if dtype not in [np.float32, Type.dynamic]:
+        relu = ov.opset8.convert(relu, np.float32)
+
+    sigm = ov.opset8.sigmoid(relu)
+
+    parameter_list = [x, y, z]
+    model = Model([sigm], parameter_list, "test")
+    return model
+
+
+class TestPytorchConversionParams(CommonMOConvertTest):
+
+    test_data = [
+        {'params_test': {'input': [(torch.Size([2, 3, 4]), torch.float32),
+                                   (torch.empty(2, 3, 4).size(), torch.float32),
+                                   (torch.empty(2, 3, 4).shape, torch.float32)]},
+         'fw_model': create_model_three_inputs(),
+         'ref_model': make_ref_model_three_inputs([2,3,4], np.float32)},
+        {'params_test': {'input': [(torch.Size([5, 2]), torch.int32),
+                                   (torch.empty(5, 2).size(), torch.int32),
+                                   (torch.empty(5, 2).shape, torch.int32)]},
+         'fw_model': create_model_three_inputs(),
+         'ref_model': make_ref_model_three_inputs([5, 2], np.int32)},
+        {'params_test': {'input': [(torch.Size([1, 3, 5]), torch.float32)]},
+         'fw_model': make_pt_model_one_input(),
+         'ref_model': make_ref_pt_model_one_input([1, 3, 5], np.float32)},
+        {'params_test': {'input': [(torch.empty(7, 3).size(), torch.int32)]},
+         'fw_model': make_pt_model_one_input(),
+         'ref_model': make_ref_pt_model_one_input([7, 3], np.int32)},
+    ]
+
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.nightly
+    def test_conversion_params(self, params, ie_device, precision, ir_version,
+                                 temp_dir, use_new_frontend, use_old_api):
+        fw_model = params['fw_model']
+        test_params = params['params_test']
+        ref_model = params['ref_model']
+
+        test_params.update({'input_model': fw_model})
+        self._test_by_ref_graph(temp_dir, test_params, ref_model, compare_tensor_names=False)
diff --git a/tests/layer_tests/ovc_python_api_tests/test_tf.py b/tests/layer_tests/ovc_python_api_tests/test_tf.py
index e8c098ff9ca..b894ec7153e 100644
--- a/tests/layer_tests/ovc_python_api_tests/test_tf.py
+++ b/tests/layer_tests/ovc_python_api_tests/test_tf.py
@@ -10,11 +10,10 @@ from openvino.runtime import PartialShape, Model, Dimension
 
 from common.mo_convert_test_class import CommonMOConvertTest
 from common.layer_test_class import CommonLayerTest
+import tensorflow as tf
 
 
 def create_tf_graph_def(tmp_dir):
-    import tensorflow as tf
-
     tf.compat.v1.reset_default_graph()
 
     with tf.compat.v1.Session() as sess:
@@ -41,8 +40,6 @@ def create_tf_graph_def(tmp_dir):
 
 
 def create_keras_model(temp_dir):
-    import tensorflow as tf
-
     tf.keras.backend.clear_session()
     tf.compat.v1.reset_default_graph()
 
@@ -69,8 +66,6 @@ def create_keras_model(temp_dir):
 
 
 def create_tf1_wrap_function(tmp_dir):
-    import tensorflow as tf
-
     def f(x, y):
         return tf.nn.sigmoid(tf.nn.relu(x + y))
 
@@ -91,7 +86,6 @@ def create_tf1_wrap_function(tmp_dir):
 
 
 def create_tf_session(tmp_dir):
-    import tensorflow as tf
     from tensorflow.python.eager.context import graph_mode
 
     with graph_mode():
@@ -119,8 +113,6 @@ def create_tf_session(tmp_dir):
 
 
 def create_tf_module(tmp_dir):
-    import tensorflow as tf
-
     class Net(tf.Module):
         def __init__(self, name=None):
             super(Net, self).__init__(name=name)
@@ -143,8 +135,6 @@ def create_tf_module(tmp_dir):
 
 
 def create_tf_module_dynamic(tmp_dir):
-    import tensorflow as tf
-
     class Net(tf.Module):
         def __init__(self, name=None):
             super(Net, self).__init__(name=name)
@@ -169,7 +159,6 @@ def create_tf_module_dynamic(tmp_dir):
 
 
 def create_keras_layer(tmp_dir):
-    import tensorflow as tf
     class LayerModel(tf.keras.layers.Layer):
 
         def __init__(self):
@@ -193,7 +182,6 @@ def create_keras_layer(tmp_dir):
 
 
 def create_keras_layer_dynamic(tmp_dir):
-    import tensorflow as tf
     class LayerModel(tf.keras.layers.Layer):
 
         def __init__(self):
@@ -219,8 +207,6 @@ def create_keras_layer_dynamic(tmp_dir):
 
 
 def create_tf_checkpoint(tmp_dir):
-    import tensorflow as tf
-
     input_names = ["Input1", "Input2"]
     input_shape = [1, 2, 3]
 
@@ -245,8 +231,6 @@ def create_tf_checkpoint(tmp_dir):
 
 
 def create_tf_function(temp_dir):
-    import tensorflow as tf
-
     @tf.function(
         input_signature=[tf.TensorSpec(shape=[1, 2, 3], dtype=tf.float32),
                          tf.TensorSpec(shape=[1, 2, 3], dtype=tf.float32)])
@@ -268,8 +252,6 @@ def create_tf_function(temp_dir):
 
 
 def create_tf_graph(temp_dir):
-    import tensorflow as tf
-
     tf.compat.v1.reset_default_graph()
 
     with tf.compat.v1.Session() as sess:
@@ -296,8 +278,6 @@ def create_tf_graph(temp_dir):
 
 
 def create_tf_saved_model_dir(temp_dir):
-    import tensorflow as tf
-
     input_names = ["Input1", "Input2"]
     input_shape = [1, 2, 3]
 
@@ -322,7 +302,6 @@ def create_tf_saved_model_dir(temp_dir):
 
 
 def create_tf_stateful_partioned_call_net(temp_dir):
-    import tensorflow as tf
     tf.compat.v1.reset_default_graph()
 
     data_shape = [1, 1, 10, 10]
@@ -359,7 +338,6 @@ def create_tf_stateful_partioned_call_net(temp_dir):
 
 
 def create_keras_layer_input_list():
-    import tensorflow as tf
     class LayerModel(tf.keras.layers.Layer):
 
         def __init__(self):
@@ -386,7 +364,6 @@ def create_keras_layer_input_list():
 
 
 def create_keras_layer_input_list_one_inp():
-    import tensorflow as tf
     class LayerModel(tf.keras.layers.Layer):
 
         def __init__(self):
@@ -408,7 +385,6 @@ def create_keras_layer_input_list_one_inp():
 
 
 def create_keras_layer_input_dict():
-    import tensorflow as tf
     class LayerModel(tf.keras.layers.Layer):
 
         def __init__(self):
@@ -434,7 +410,6 @@ def create_keras_layer_input_dict():
 
 
 def create_keras_layer_input_dict_one_inp():
-    import tensorflow as tf
     class LayerModel(tf.keras.layers.Layer):
 
         def __init__(self):
@@ -522,7 +497,6 @@ def create_keras_layer_with_input_shapes_case4(tmp_dir):
 
 
 def create_keras_layer_with_tf_function_call(tmp_dir):
-    import tensorflow as tf
     class LayerModel(tf.Module):
         def __init__(self):
             super(LayerModel, self).__init__()
@@ -538,7 +512,6 @@ def create_keras_layer_with_tf_function_call(tmp_dir):
 
 
 def create_keras_layer_with_tf_function_call_default_compressed_to_fp16(tmp_dir):
-    import tensorflow as tf
     class LayerModel(tf.Module):
         def __init__(self):
             super(LayerModel, self).__init__()
@@ -554,7 +527,6 @@ def create_keras_layer_with_tf_function_call_default_compressed_to_fp16(tmp_dir)
 
 
 def create_keras_layer_with_tf_function_call_no_signature(tmp_dir):
-    import tensorflow as tf
     class LayerModel(tf.Module):
         def __init__(self):
             super(LayerModel, self).__init__()
@@ -572,7 +544,6 @@ def create_keras_layer_with_tf_function_call_no_signature(tmp_dir):
 
 
 def create_keras_layer_with_tf_function_call_no_signature_single_input(tmp_dir):
-    import tensorflow as tf
     class LayerModel(tf.Module):
         def __init__(self):
             super(LayerModel, self).__init__()
@@ -590,7 +561,6 @@ def create_keras_layer_with_tf_function_call_no_signature_single_input(tmp_dir):
 
 
 def create_keras_layer_with_string_tensor(tmp_dir):
-    import tensorflow as tf
     class LayerModel(tf.Module):
         def __init__(self):
             super(LayerModel, self).__init__()
@@ -612,6 +582,69 @@ def create_keras_layer_with_string_tensor(tmp_dir):
     return model, model_ref, {}
 
 
+def create_tf_model_three_inputs(shape=[1, 2, 3, 4], type=tf.float32):
+    tf.compat.v1.reset_default_graph()
+
+    with tf.compat.v1.Session() as sess:
+        inp1 = tf.compat.v1.placeholder(type, shape, 'Input1')
+        inp2 = tf.compat.v1.placeholder(type, shape, 'Input2')
+        inp3 = tf.compat.v1.placeholder(type, shape, 'Input3')
+
+        relu1 = tf.nn.relu(inp1, name='Relu1')
+        relu2 = tf.nn.relu(inp2, name='Relu2')
+        relu3 = tf.nn.relu(inp3, name='Relu3')
+
+        add = relu1 + relu2 + relu3
+
+        tf.compat.v1.global_variables_initializer()
+        tf_net = sess.graph
+    return tf_net
+
+
+def create_ref_model_three_inputs(shape=[1, 2, 3, 4], dtype=np.float32):
+    inp1 = ov.opset8.parameter(PartialShape(
+        shape), name="Input1", dtype=dtype)
+    inp2 = ov.opset8.parameter(PartialShape(
+        shape), name="Input2", dtype=dtype)
+    inp3 = ov.opset8.parameter(PartialShape(
+        shape), name="Input3", dtype=dtype)
+
+    relu1 = ov.opset8.relu(inp1)
+    relu2 = ov.opset8.relu(inp2)
+    relu3 = ov.opset8.relu(inp3)
+
+    add1 = ov.opset8.add(relu1, relu2)
+    add2 = ov.opset8.add(add1, relu3)
+
+    parameter_list = [inp1, inp2, inp3]
+    model = Model([add2], parameter_list, "test")
+    return model
+
+
+def create_tf_model_single_input(shape=[1, 2, 3, 4], type=tf.float32):
+    tf.compat.v1.reset_default_graph()
+
+    with tf.compat.v1.Session() as sess:
+        inp = tf.compat.v1.placeholder(type, shape, 'Input')
+        relu = tf.nn.relu(inp, name='Relu')
+        output = tf.nn.sigmoid(relu, name='Sigmoid')
+
+        tf.compat.v1.global_variables_initializer()
+        tf_net = sess.graph
+
+    return tf_net
+
+
+def create_ref_model_single_input(shape=[1, 2, 3, 4], dtype=np.float32):
+    inp = ov.opset8.parameter(PartialShape(
+        shape), name="Input", dtype=dtype)
+    relu = ov.opset8.relu(inp)
+    sigm = ov.opset8.sigmoid(relu)
+    parameter_list = [inp]
+    model = Model([sigm], parameter_list, "test")
+    return model
+
+
 class TestMoConvertTF(CommonMOConvertTest):
     test_data = [
         # TF2
@@ -667,7 +700,6 @@ class TestMoConvertTF(CommonMOConvertTest):
         self._test_by_ref_graph(temp_dir, test_params, graph_ref, compare_tensor_names=False)
 
     def test_zero_copy(self, ie_device, precision, ir_version, temp_dir):
-        import tensorflow as tf
         from openvino.tools.ovc import convert_model
         from openvino.runtime import compile_model
         class LayerModel(tf.Module):
@@ -716,7 +748,6 @@ class TestMoConvertTF(CommonMOConvertTest):
         assert np.array_equal(ov_infer2['Identity:0'], [ 0., 8., 16.])
 
     def test_turn_off_sharing(self, ie_device, precision, ir_version, temp_dir):
-        import tensorflow as tf
         from openvino.tools.ovc import convert_model
         from openvino.runtime import compile_model
         class LayerModel(tf.Module):
@@ -767,7 +798,6 @@ class TestMoConvertTF(CommonMOConvertTest):
     def test_memory_loss(self, ie_device, precision, ir_version, temp_dir):
         # This test checks that the memory allocated for constants
         # is not lost after returning the model from convert_model() method.
-        import tensorflow as tf
         tf.compat.v1.reset_default_graph()
 
         from openvino.tools.ovc import convert_model
@@ -820,7 +850,6 @@ class TestMoConvertTF(CommonMOConvertTest):
         assert CommonLayerTest().compare_ie_results_with_framework(ov_infer1, {"add:0": [2.6, 9.6, 12.4]}, eps)
 
     def test_scalar(self, ie_device, precision, ir_version, temp_dir):
-        import tensorflow as tf
         tf.compat.v1.reset_default_graph()
 
         from openvino.tools.ovc import convert_model
@@ -861,7 +890,6 @@ class TestMoConvertTF(CommonMOConvertTest):
         assert CommonLayerTest().compare_ie_results_with_framework(ov_infer, {"Identity:0": 3.2}, eps)
 
     def test_unnamed_variable(self, ie_device, precision, ir_version, temp_dir):
-        import tensorflow as tf
         tf.compat.v1.reset_default_graph()
 
         from openvino.tools.ovc import convert_model
@@ -905,7 +933,6 @@ class TFConvertTest(unittest.TestCase):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_tf_function_no_signature(self):
-        import tensorflow as tf
         from openvino.tools.ovc import convert_model
 
         @tf.function()
@@ -920,8 +947,6 @@ class TFConvertTest(unittest.TestCase):
 class TestTFLoadByModel(unittest.TestCase):
     def test_load_by_model_tf_graph_iterator(self):
         def simple_tf_model():
-            import tensorflow as tf
-
             tf.compat.v1.reset_default_graph()
 
             with tf.compat.v1.Session() as sess:
@@ -955,3 +980,37 @@ class TestTFConvertRaises(unittest.TestCase):
         # check that it accepts specified names as is without parsing into 2 different inputs
         with self.assertRaisesRegex(Exception, 'No node with name Input1\[1, 2, 3\],Input2\[1, 2, 3\]'):
             convert_model(tf_model, input='Input1[1, 2, 3],Input2[1, 2, 3]')
+
+
+class TestTFConversionParams(CommonMOConvertTest):
+    test_data = [
+        {'params_test': {'input': [tf.shape(tf.zeros((2, 3, 4))), tf.zeros((2, 3, 4)).shape, tf.TensorShape((2, 3, 4))]},
+         'fw_model': create_tf_model_three_inputs([1, 2, 3, 4]),
+         'ref_model': create_ref_model_three_inputs([2, 3, 4])},
+        {'params_test': {'input': [tf.float32, tf.float32, tf.float32]},
+         'fw_model': create_tf_model_three_inputs([2, 3], tf.int32),
+         'ref_model': create_ref_model_three_inputs([2, 3], np.float32)},
+        {'params_test': {'input': tf.shape(tf.zeros((5, 8, 2)))},
+         'fw_model': create_tf_model_single_input(),
+         'ref_model': create_ref_model_single_input([5, 8, 2])},
+        {'params_test': {'input': tf.zeros((9, 2)).shape},
+         'fw_model': create_tf_model_single_input(),
+         'ref_model': create_ref_model_single_input([9, 2])},
+        {'params_test': {'input': tf.TensorShape((4, 8, 3))},
+         'fw_model': create_tf_model_single_input(),
+         'ref_model': create_ref_model_single_input([4, 8, 3])},
+        {'params_test': {'input': tf.int32},
+         'fw_model': create_tf_model_single_input(),
+         'ref_model': create_ref_model_single_input([1, 2, 3, 4], np.int32)}
+    ]
+
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.nightly
+    def test_mo_convert_tf_model(self, params, ie_device, precision, ir_version,
+                                 temp_dir, use_new_frontend, use_old_api):
+        fw_model = params['fw_model']
+        test_params = params['params_test']
+        ref_model = params['ref_model']
+
+        test_params.update({'input_model': fw_model})
+        self._test_by_ref_graph(temp_dir, test_params, ref_model, compare_tensor_names=False)
\ No newline at end of file
diff --git a/tools/ovc/openvino/tools/ovc/cli_parser.py b/tools/ovc/openvino/tools/ovc/cli_parser.py
index 7bf03f5f548..87fc0225206 100644
--- a/tools/ovc/openvino/tools/ovc/cli_parser.py
+++ b/tools/ovc/openvino/tools/ovc/cli_parser.py
@@ -14,22 +14,13 @@ from openvino.runtime import PartialShape, Dimension, Shape, Type  # pylint: dis
 import openvino
 from openvino.tools.ovc.error import Error
 from openvino.tools.ovc.help import get_convert_model_help_specifics
+from openvino.tools.ovc.moc_frontend.shape_utils import to_partial_shape, is_shape_type
+from openvino.tools.ovc.moc_frontend.type_utils import to_ov_type, is_type
 from openvino.tools.ovc.utils import get_mo_root_dir
 
 # Helper class for storing input cut information
 _InputCutInfo = namedtuple("InputCutInfo", ["name", "shape", "type", "value"], defaults=[None, None, None, None])
 
-def is_shape_type(value):
-    if isinstance(value, PartialShape):
-        return True
-    if isinstance(value, Shape):
-        return True
-    if isinstance(value, list) or isinstance(value, tuple):
-        for dim in value:
-            if not (isinstance(dim, Dimension) or isinstance(dim, int)):
-                return False
-        return True
-    return False
 
 def single_input_to_input_cut_info(input: [str, tuple, list, PartialShape, Type, type]):
     """
@@ -43,7 +34,7 @@ def single_input_to_input_cut_info(input: [str, tuple, list, PartialShape, Type,
     if isinstance(input, (tuple, list)) or is_shape_type(input):
         # If input represents list with shape, wrap it to list. Single PartialShape also goes to this condition.
         # Check of all dimensions will be in is_shape_type(val) method below
-        if len(input) > 0 and isinstance(input[0], (int, Dimension)) or isinstance(input, PartialShape):
+        if is_shape_type(input):
             input = [input]
 
         # Check values of tuple or list and collect to InputCutInfo
@@ -55,14 +46,14 @@ def single_input_to_input_cut_info(input: [str, tuple, list, PartialShape, Type,
                 if name is not None:
                     raise Exception("More than one input name provided: {}".format(input))
                 name = val
-            elif isinstance(val, (type, Type)):
+            elif is_type(val):
                 if inp_type is not None:
                     raise Exception("More than one input type provided: {}".format(input))
-                inp_type = val
+                inp_type = to_ov_type(val)
             elif is_shape_type(val) or val is None:
                 if shape is not None:
                     raise Exception("More than one input shape provided: {}".format(input))
-                shape = PartialShape(val) if val is not None else None
+                shape = to_partial_shape(val) if val is not None else None
             else:
                 raise Exception("Incorrect input parameters provided. Expected tuple with input name, "
                                 "input type or input shape. Got unknown object: {}".format(val))
@@ -72,14 +63,16 @@ def single_input_to_input_cut_info(input: [str, tuple, list, PartialShape, Type,
                              inp_type,
                              None)
     # Case when only type is set
-    if isinstance(input, (type, Type)):
-        return _InputCutInfo(None, None, input, None) # pylint: disable=no-member
+    if is_type(input):
+        return _InputCutInfo(None, None, to_ov_type(input), None) # pylint: disable=no-member
 
     # We don't expect here single unnamed value. If list of int is set it is considered as shape.
     # Setting of value is expected only using InputCutInfo or string analog.
 
     raise Exception("Unexpected object provided for input. Expected tuple, Shape, PartialShape, Type or str. Got {}".format(type(input)))
 
+
+
 def is_single_input(input: [tuple, list]):
     """
     Checks if input has parameters for single input.
@@ -94,14 +87,14 @@ def is_single_input(input: [tuple, list]):
             if name is not None:
                 return False
             name = val
-        elif isinstance(val, (type, Type)):
+        elif is_type(val):
             if inp_type is not None:
                 return False
-            inp_type = val
+            inp_type = to_ov_type(val)
         elif is_shape_type(val):
             if shape is not None:
                 return False
-            shape = PartialShape(val)
+            shape = to_partial_shape(val)
         else:
             return False
     return True
diff --git a/tools/ovc/openvino/tools/ovc/moc_frontend/shape_utils.py b/tools/ovc/openvino/tools/ovc/moc_frontend/shape_utils.py
index defbe552b1d..68599646909 100644
--- a/tools/ovc/openvino/tools/ovc/moc_frontend/shape_utils.py
+++ b/tools/ovc/openvino/tools/ovc/moc_frontend/shape_utils.py
@@ -1,8 +1,11 @@
 # Copyright (C) 2018-2023 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+import sys
+
 import numpy as np
-from openvino.runtime import PartialShape, Dimension # pylint: disable=no-name-in-module,import-error
+from openvino.runtime import Shape, PartialShape, Dimension  # pylint: disable=no-name-in-module,import-error
+
 from openvino.tools.ovc.error import Error
 
 
@@ -62,3 +65,46 @@ def get_dynamic_dims(shape: [PartialShape, list, tuple]):
                 dynamic_dims.append(idx)
 
     return dynamic_dims
+
+
+def tensor_to_int_list(tensor):
+    assert hasattr(tensor, 'numpy'), "Could not get value of provided tensor: {}".format(tensor)
+    tensor_numpy = tensor.numpy()
+    assert tensor_numpy.dtype == np.int32, "Unexpected type of provided tensor. Expected int32, got: {}".format(
+        tensor_numpy.dtype)
+    return tensor_numpy.tolist()
+
+def to_partial_shape(shape):
+    if 'tensorflow' in sys.modules:
+        import tensorflow as tf
+        if isinstance(shape, tf.Tensor):
+            return PartialShape(tensor_to_int_list(shape))
+        if isinstance(shape, tf.TensorShape):
+            return PartialShape(list(shape))
+    if 'paddle' in sys.modules:
+        import paddle
+        if isinstance(shape, paddle.Tensor):
+            return PartialShape(tensor_to_int_list(shape))
+    return PartialShape(shape)
+
+
+def is_shape_type(value):
+    if isinstance(value, PartialShape):
+        return True
+    if 'tensorflow' in sys.modules:
+        import tensorflow as tf
+        if isinstance(value, (tf.TensorShape, tf.Tensor)):
+            return True
+    if 'paddle' in sys.modules:
+        import paddle
+        if isinstance(value, paddle.Tensor):
+            return True
+    if isinstance(value, Shape):
+        return True
+    if isinstance(value, list) or isinstance(value, tuple):
+        for dim in value:
+            if not (isinstance(dim, Dimension) or isinstance(dim, int)):
+                return False
+        return True
+    return False
+
diff --git a/tools/ovc/openvino/tools/ovc/moc_frontend/type_utils.py b/tools/ovc/openvino/tools/ovc/moc_frontend/type_utils.py
new file mode 100644
index 00000000000..954143fda9b
--- /dev/null
+++ b/tools/ovc/openvino/tools/ovc/moc_frontend/type_utils.py
@@ -0,0 +1,81 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import sys
+
+from openvino.runtime import Type
+
+import openvino as ov
+
+
+def is_type(val):
+    if isinstance(val, (type, Type)):
+        return True
+    if 'tensorflow' in sys.modules:
+        import tensorflow as tf
+        if isinstance(val, tf.dtypes.DType):
+            return True
+    if 'torch' in sys.modules:
+        import torch
+        if isinstance(val, torch.dtype):
+            return True
+    if 'paddle' in sys.modules:
+        import paddle
+        if isinstance(val, paddle.dtype):
+            return True
+    return False
+
+
+def to_ov_type(val):
+    if isinstance(val, Type):
+        return val
+    if isinstance(val, type):
+        return Type(val)
+    if 'tensorflow' in sys.modules:
+        import tensorflow as tf
+        if isinstance(val, tf.dtypes.DType):
+            return Type(val.as_numpy_dtype())
+    if 'torch' in sys.modules:
+        import torch
+
+        if isinstance(val, torch.dtype):
+            torch_to_ov_type = {
+                torch.float32: ov.Type.f32,
+                torch.float16: ov.Type.f16,
+                torch.float64: ov.Type.f64,
+                torch.bfloat16: ov.Type.bf16,
+                torch.uint8: ov.Type.u8,
+                torch.int8: ov.Type.i8,
+                torch.int16: ov.Type.i16,
+                torch.int32: ov.Type.i32,
+                torch.int64: ov.Type.i64,
+                torch.bool: ov.Type.boolean,
+            }
+            if val not in torch_to_ov_type:
+                raise Exception("The provided data time is not supported {}.".format(val))
+
+            return torch_to_ov_type[val]
+
+    if 'paddle' in sys.modules:
+        import paddle
+
+        if isinstance(val, paddle.dtype):
+            paddle_to_ov_type = {
+                paddle.float32: ov.Type.f32,
+                paddle.float16: ov.Type.f16,
+                paddle.float64: ov.Type.f64,
+                paddle.bfloat16: ov.Type.bf16,
+                paddle.uint8: ov.Type.u8,
+                paddle.int8: ov.Type.i8,
+                paddle.int16: ov.Type.i16,
+                paddle.int32: ov.Type.i32,
+                paddle.int64: ov.Type.i64,
+                paddle.bool: ov.Type.boolean,
+            }
+
+            if val not in paddle_to_ov_type:
+                raise Exception("The provided data time is not supported {}.".format(val))
+
+            return paddle_to_ov_type[val]
+    raise Exception("Unexpected type object. Expected ov.Type, np.dtype, tf.dtypes.DType. Got {}".format(type(val)))
+
diff --git a/tools/ovc/unit_tests/ovc/package_BOM.txt b/tools/ovc/unit_tests/ovc/package_BOM.txt
index 5c8c73a2ac8..c7b37c2c147 100644
--- a/tools/ovc/unit_tests/ovc/package_BOM.txt
+++ b/tools/ovc/unit_tests/ovc/package_BOM.txt
@@ -22,6 +22,7 @@ openvino/tools/ovc/moc_frontend/pipeline.py
 openvino/tools/ovc/moc_frontend/preprocessing.py
 openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py
 openvino/tools/ovc/moc_frontend/shape_utils.py
+openvino/tools/ovc/moc_frontend/type_utils.py
 openvino/tools/ovc/ovc.py
 openvino/tools/ovc/telemetry_params.py
 openvino/tools/ovc/telemetry_stub.py