diff --git a/src/frontends/pytorch/src/op/is_nonzero.cpp b/src/frontends/pytorch/src/op/is_nonzero.cpp
new file mode 100644
index 00000000000000..7635860e36d52f
--- /dev/null
+++ b/src/frontends/pytorch/src/op/is_nonzero.cpp
@@ -0,0 +1,53 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/convert.hpp"
+#include "openvino/op/equal.hpp"
+#include "openvino/op/logical_and.hpp"
+#include "openvino/op/logical_or.hpp"
+#include "openvino/op/not_equal.hpp"
+#include "openvino/op/shape_of.hpp"
+#include "pt_framework_node.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+using namespace ov::op;
+
+OutputVector translate_is_nonzero(const NodeContext& context) {
+    num_inputs_check(context, 1, 1);
+    auto const_0 = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {0}));
+    auto const_1 = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {1}));
+    auto input = context.get_input(0);
+
+    // check if length input is 1
+    auto input_shape = context.mark_node(std::make_shared<v3::ShapeOf>(input, element::i32));
+    auto is_length_one = context.mark_node(std::make_shared<v1::Equal>(input_shape, const_1));
+
+    // check if element is not equal to 0 or false
+    auto zero_tensor = context.mark_node(v0::Constant::create(element::f32, Shape{1}, {0.0}));
+    auto false_tensor = context.mark_node(v0::Constant::create(element::boolean, Shape{1}, {false}));
+
+    // perform type conversion
+    auto converted_input = context.mark_node(std::make_shared<v0::Convert>(input, element::f32));
+
+    auto is_nonzero_numeric = context.mark_node(std::make_shared<v1::NotEqual>(converted_input, zero_tensor));
+    auto is_nonzero_boolean = context.mark_node(std::make_shared<v1::NotEqual>(input, false_tensor));
+
+    auto final_result = context.mark_node(std::make_shared<v1::LogicalAnd>(
+        is_length_one,
+        context.mark_node(std::make_shared<v1::LogicalOr>(is_nonzero_numeric, is_nonzero_boolean))));
+
+    return {final_result};
+};
+
+}  // 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 75665ffe8d4d14..eaf66fa0cd8094 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -88,6 +88,7 @@ OP_CONVERTER(translate_index_put_);
 OP_CONVERTER(translate_index_select);
 OP_CONVERTER(translate_instance_norm);
 OP_CONVERTER(translate_int);
+OP_CONVERTER(translate_is_nonzero);
 OP_CONVERTER(translate_layer_norm);
 OP_CONVERTER(translate_len);
 OP_CONVERTER(translate_linalg_norm);
@@ -355,6 +356,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::Int", op::translate_int},
         {"aten::IntImplicit", op::translate_int},
         {"aten::is_grad_enabled", op::return_false_scalar},
+        {"aten::is_nonzero", op::translate_is_nonzero},
         {"aten::item", op::translate_1to1_match_1_inputs<opset10::Squeeze>},
         {"aten::layer_norm", op::translate_layer_norm},
         {"aten::le", op::translate_1to1_match_2_inputs_align_types<opset10::LessEqual>},
diff --git a/tests/layer_tests/pytorch_tests/test_is_nonzero.py b/tests/layer_tests/pytorch_tests/test_is_nonzero.py
new file mode 100644
index 00000000000000..2cd63d6a80ad00
--- /dev/null
+++ b/tests/layer_tests/pytorch_tests/test_is_nonzero.py
@@ -0,0 +1,32 @@
+# 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
+
+
+@pytest.mark.parametrize('input_tensor', (np.array([0.]), np.array([1.5]), np.array([False]), np.array([3]), np.array([1, 3, 5])))
+class TestIsNonZero(PytorchLayerTest):
+
+    def _prepare_input(self):
+        input_tensor = self.input_tensor
+        return (input_tensor.astype(np.int64),)
+
+    def create_model(self):
+        class aten_is_nonzero(torch.nn.Module):
+
+            def forward(self, input_tensor):
+                return torch.is_nonzero(input_tensor)
+
+        ref_net = None
+
+        return aten_is_nonzero(), ref_net, "aten::is_nonzero"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_is_nonzero(self, ie_device, precision, ir_version, input_tensor):
+        self.input_tensor = input_tensor
+        self._test(*self.create_model(), ie_device, precision, ir_version)