diff --git a/src/frontends/pytorch/src/op_table.cpp b/src/frontends/pytorch/src/op_table.cpp
index 00e3a55b0bc327..79b34e8308619a 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -293,6 +293,7 @@ OP_CONVERTER(translate_layer_norm_fx);
 OP_CONVERTER(translate_leaky_relu_fx);
 OP_CONVERTER(translate_log_sigmoid_fx);
 OP_CONVERTER(translate_log_softmax_fx);
+OP_CONVERTER(translate_logaddexp);
 OP_CONVERTER(translate_max_dim_fx);
 OP_CONVERTER(translate_max_pool2d_fx);
 OP_CONVERTER(translate_max_pool3d_fx);
@@ -546,6 +547,7 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::logical_xor", op::translate_xor},
         {"aten::log_sigmoid", op::translate_log_sigmoid},
         {"aten::log_softmax", op::translate_log_softmax},
+        {"aten::logaddexp", op::translate_logaddexp},
         {"aten::log1p", op::optional_out<op::translate_log1p, 1>},
         {"aten::log1p_", op::inplace_op<op::translate_log1p>},
         {"aten::log2", op::optional_out<op::translate_log2, 1>},
diff --git a/tests/layer_tests/pytorch_tests/test_logaddexp.py b/tests/layer_tests/pytorch_tests/test_logaddexp.py
new file mode 100644
index 00000000000000..023d85dd08dd52
--- /dev/null
+++ b/tests/layer_tests/pytorch_tests/test_logaddexp.py
@@ -0,0 +1,130 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import numpy as np
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestLogAddExp(PytorchLayerTest):
+    def _prepare_input(self, input1, input2, dtype="float32"):
+        """Prepare inputs for logaddexp testing"""
+        return (np.array(input1).astype(dtype), np.array(input2).astype(dtype))
+
+    def create_model(self, dtype=None):
+        import torch
+
+        dtype_map = {
+            "float32": torch.float32,
+            "float64": torch.float64,
+        }
+
+        class LogAddExpModel(torch.nn.Module):
+            def __init__(self, dtype=None):
+                super(LogAddExpModel, self).__init__()
+                self.dtype = dtype_map.get(dtype) if dtype else None
+
+            def forward(self, x, y):
+                if self.dtype:
+                    x = x.to(self.dtype)
+                    y = y.to(self.dtype)
+                return torch.logaddexp(x, y)
+
+        model_class = LogAddExpModel(dtype)
+        ref_net = None
+
+        return model_class, ref_net, "aten::logaddexp"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "float32",
+            "float64",
+        ],
+    )
+    @pytest.mark.parametrize(
+        "input1,input2",
+        [
+            # Basic cases
+            (0.0, 0.0),        # log(exp(0) + exp(0)) = log(2)
+            (1.0, 1.0),        # log(exp(1) + exp(1)) = log(2*e)
+            (-1.0, -1.0),      # log(exp(-1) + exp(-1)) = log(2/e)
+            
+            # One large, one small number
+            (100.0, 0.0),      # Tests handling of large differences
+            (-100.0, 0.0),     # Tests handling of negative large differences
+            
+            # Both large numbers
+            (100.0, 100.0),    # Tests numerical stability with large numbers
+            (-100.0, -100.0),  # Tests numerical stability with large negative numbers
+            
+            # Numbers with different signs
+            (1.0, -1.0),       # Tests mixed positive/negative
+            (-1.0, 1.0),       # Tests mixed negative/positive
+            
+            # Near-zero cases
+            (1e-7, 1e-7),      # Tests handling of very small numbers
+            (-1e-7, -1e-7),    # Tests handling of very small negative numbers
+        ],
+    )
+    def test_logaddexp_basic(self, dtype, input1, input2, ie_device, precision, ir_version):
+        self._test(
+            *self.create_model(dtype),
+            ie_device,
+            precision,
+            ir_version,
+            kwargs_to_prepare_input={"input1": input1, "input2": input2, "dtype": dtype},
+            rtol=1e-5  # Relative tolerance for floating point comparisons
+        )
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "float32",
+            "float64",
+        ],
+    )
+    @pytest.mark.parametrize(
+        "shape",
+        [
+            (3,),           # 1D array
+            (2, 3),         # 2D array
+            (2, 2, 2),      # 3D array
+            (1, 1),         # Broadcasting test
+            (3, 1),         # Broadcasting test
+            (1, 3),         # Broadcasting test
+        ],
+    )
+    def test_logaddexp_shapes(self, dtype, shape, ie_device, precision, ir_version):
+        # Generate random inputs within a reasonable range
+        input1 = np.random.uniform(-10, 10, shape)
+        input2 = np.random.uniform(-10, 10, shape)
+        
+        self._test(
+            *self.create_model(dtype),
+            ie_device,
+            precision,
+            ir_version,
+            kwargs_to_prepare_input={"input1": input1, "input2": input2, "dtype": dtype},
+            rtol=1e-5
+        )
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_logaddexp_broadcasting(self, ie_device, precision, ir_version):
+        # Test broadcasting with different shapes
+        input1 = np.array([[1.0, 2.0, 3.0]], dtype=np.float32)  # Shape (1, 3)
+        input2 = np.array([[1.0], [2.0]], dtype=np.float32)     # Shape (2, 1)
+        
+        self._test(
+            *self.create_model("float32"),
+            ie_device,
+            precision,
+            ir_version,
+            kwargs_to_prepare_input={"input1": input1, "input2": input2, "dtype": "float32"},
+            rtol=1e-5
+        )
\ No newline at end of file