diff --git a/src/frontends/pytorch/src/op/all.cpp b/src/frontends/pytorch/src/op/all.cpp
new file mode 100644
index 00000000000..55c830df524
--- /dev/null
+++ b/src/frontends/pytorch/src/op/all.cpp
@@ -0,0 +1,41 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/opsets/opset10.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+OutputVector translate_all(const NodeContext& context) {
+    num_inputs_check(context, 1, 3);
+    const auto input_tensor = context.get_input(0);
+
+    element::Type output_dtype = element::boolean;
+    if (input_tensor.get_element_type() == element::u8) {
+        output_dtype = element::u8;
+    }
+
+    bool keep_dims = false;
+    ov::Output<ov::Node> axes;
+    if (context.get_input_size() == 1) {
+        axes = get_axes_range(context, 0);
+    } else {
+        const auto dim = context.const_input<int64_t>(1);
+        axes = context.mark_node(opset10::Constant::create(element::i64, Shape{1}, {dim}));
+        if (!context.input_is_none(2)) {
+            keep_dims = context.const_input<bool>(2);
+        }
+    }
+
+    const auto all_nonzero = context.mark_node(std::make_shared<opset10::ReduceProd>(input_tensor, axes, keep_dims));
+    return {context.mark_node(std::make_shared<opset10::Convert>(all_nonzero, output_dtype))};
+};
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/pytorch/src/op_table.cpp b/src/frontends/pytorch/src/op_table.cpp
index e8698860f97..f23652f2eee 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -19,6 +19,7 @@ OP_CONVERTER(translate_adaptive_max_pool2d);
 OP_CONVERTER(translate_add);
 OP_CONVERTER(translate_addcmul);
 OP_CONVERTER(translate_addmm);
+OP_CONVERTER(translate_all);
 OP_CONVERTER(translate_arange);
 OP_CONVERTER(translate_argsort);
 OP_CONVERTER(translate_as_tensor);
@@ -159,6 +160,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::add_", op::inplace_op<op::translate_add>},
         {"aten::addcmul", op::translate_addcmul},
         {"aten::addmm", op::translate_addmm},
+        {"aten::all", op::translate_all},
         {"aten::argsort", op::translate_argsort},
         {"aten::arange", op::translate_arange},
         {"aten::as_tensor", op::translate_as_tensor},
diff --git a/tests/layer_tests/pytorch_tests/test_all.py b/tests/layer_tests/pytorch_tests/test_all.py
new file mode 100644
index 00000000000..b5255f197cf
--- /dev/null
+++ b/tests/layer_tests/pytorch_tests/test_all.py
@@ -0,0 +1,75 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import torch
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+class aten_all_noparam(torch.nn.Module):
+    def __init__(self) -> None:
+        torch.nn.Module.__init__(self)
+
+    def forward(self, input_tensor):
+        return torch.all(input_tensor)
+
+class aten_all(torch.nn.Module):
+    def __init__(self, dim, keepdim) -> None:
+        torch.nn.Module.__init__(self)
+        self.dim = dim
+        self.keepdim = keepdim
+
+    def forward(self, input_tensor):
+        return torch.all(
+            input_tensor, 
+            dim = self.dim
+        ) if self.keepdim is None else torch.all(
+            input_tensor, 
+            dim = self.dim,
+            keepdim = self.keepdim
+        )
+
+class TestAll(PytorchLayerTest):
+    def _prepare_input(self):
+        return (self.input_tensor,)
+
+    @pytest.mark.parametrize("input_tensor", [
+        np.eye(5,5),
+        np.zeros((5, 5)),
+        np.zeros((9,8)) + 1,
+        np.random.randint(0, 2, (5, 9, 7)),
+        np.random.randint(0, 2, (10, 13, 11)),
+        np.random.randint(0, 2, (8, 7, 6, 5, 4)),
+        np.random.randint(0, 2, (11, 11), dtype=np.uint8),
+        np.random.randint(0, 2, (7, 7), dtype=np.uint8),
+    ])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_all_noparams(self, input_tensor, ie_device, precision, ir_version):
+        self.input_tensor = input_tensor
+        self._test(aten_all_noparam(), None, "aten::all", 
+                ie_device, precision, ir_version, trace_model=True, freeze_model=False)
+            
+    @pytest.mark.parametrize("input_tensor", [
+        np.eye(5,5),
+        np.zeros((5, 5)),
+        np.zeros((9,8)) + 1,
+        np.random.randint(0, 2, (5, 9, 7)),
+        np.random.randint(0, 2, (10, 13, 11)),
+        np.random.randint(0, 2, (8, 7, 6, 5, 4)),
+        np.random.randint(0, 2, (11, 11), dtype=np.uint8),
+        np.random.randint(0, 2, (7, 7), dtype=np.uint8),
+    ])
+    @pytest.mark.parametrize("keepdim", [
+        True,
+        False,
+        None
+    ])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_all(self, input_tensor, keepdim, ie_device, precision, ir_version):
+        self.input_tensor = input_tensor
+        for dim in range(len(input_tensor.shape)):
+            self._test(aten_all(dim, keepdim), None, "aten::all", 
+                    ie_device, precision, ir_version, trace_model=True, freeze_model=False)