diff --git a/model-optimizer/mo/middle/passes/fusing/fuse_linear_ops.py b/model-optimizer/mo/middle/passes/fusing/fuse_linear_ops.py
index 0c021b19e2fb91..1c011556c01e21 100644
--- a/model-optimizer/mo/middle/passes/fusing/fuse_linear_ops.py
+++ b/model-optimizer/mo/middle/passes/fusing/fuse_linear_ops.py
@@ -111,6 +111,17 @@ def _fuse_mul(graph: Graph, node: Node, fuse_nodes: list, backward: bool = True)
         weights_port.get_connection().set_source(w_mul.out_port(0))
         w_const.connect(w_mul.in_port(tensor_port.idx))
 
+        fuse_node_in_data = fuse_node.in_node(weights_port.idx)
+        w_const_out_data = w_const.node.out_node(w_const.idx)
+
+        # During this reconnection new data node name is copied from the data node
+        # outgoing from w_const port. Duplicate names of data nodes lead to appearing
+        # of duplicate op node names after constant folding. So we should manually
+        # set a unique name for the new data node.
+        if fuse_node_in_data.soft_get('name') == w_const_out_data.soft_get('name') and \
+                fuse_node_in_data.soft_get('name', None) is not None:
+            fuse_node.in_node(weights_port.idx)['name'] = graph.unique_id(mul_name)
+
         # If we fuse in backward direction we should multiply biases if they exists
         if backward and len(fuse_node.in_ports()) == 3 and not fuse_node.in_port(2).disconnected() and \
                 not fuse_node.has_and_set('shape_input'):
diff --git a/model-optimizer/unit_tests/mo/middle/passes/fusing/fuse_linear_ops_test.py b/model-optimizer/unit_tests/mo/middle/passes/fusing/fuse_linear_ops_test.py
index 6626d61dba0690..76c1b67f8f7f38 100644
--- a/model-optimizer/unit_tests/mo/middle/passes/fusing/fuse_linear_ops_test.py
+++ b/model-optimizer/unit_tests/mo/middle/passes/fusing/fuse_linear_ops_test.py
@@ -817,6 +817,55 @@ def test_fuse_mul_to_deconv_1(self):
         (flag, resp) = compare_graphs(graph, graph_ref, 'placeholder_1', 'placeholder_1')
         self.assertTrue(flag, resp)
 
+    def test_fuse_mul_data_nodes_names(self):
+        graph = build_graph(nodes_attributes,
+                            [('placeholder_1', 'placeholder_1_data'),
+                             ('placeholder_1_data', 'mul_1'),
+                             ('const_mul_1_w', 'mul_1_w'),
+                             ('mul_1_w', 'mul_1'),
+                             ('mul_1', 'mul_1_data'),
+                             ('mul_1_data', 'conv_1'),
+                             ('const_conv_1_w', 'conv_1_w'),
+                             ('const_conv_1_b', 'conv_1_b'),
+                             ('conv_1_w', 'conv_1'),
+                             ('conv_1_b', 'conv_1'),
+                             ('conv_1', 'conv_1_data'),
+                             ('conv_1_data', 'op_output')
+                             ],
+                            {'placeholder_1_data': {'shape': np.array([1, 227, 227, 3])},
+                             'mul_1_data': {'shape': np.array([1, 227, 227, 3])},
+                             'const_mul_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
+                             'mul_1_w': {'shape': np.array([3]), 'value': np.array([1, 2, 3])},
+                             'const_conv_1_w': {'shape': np.array([11, 11, 3, 96]),
+                                                'value': np.ones((11, 11, 3, 96))},
+                             'conv_1_w': {'shape': np.array([11, 11, 3, 96]), 'value': np.ones((11, 11, 3, 96)),
+                                          'output_channel_dim': 3, 'input_channel_dim': 2,
+                                          'dims_number': 4},
+                             'const_conv_1_b': {'shape': np.array([96]), 'value': np.zeros(96)},
+                             'conv_1_b': {'shape': np.array([96]), 'value': np.zeros(96)},
+                             'conv_1_data': {}
+                             })
+
+        _fuse_mul(graph, Node(graph, 'mul_1'), [Node(graph, 'conv_1')], backward=False)
+
+        conv_node = Node(graph, 'conv_1')
+        conv_in_data_name = conv_node.in_node(1)['name']
+        const_node = Node(graph, 'const_conv_1_w')
+        const_out_data_name = const_node.out_node(0)['name']
+        mul_node = Node(graph, 'mul_1')
+        conv_in_data = conv_node.in_node(1)
+
+        # Check that transformation doesn't produce identical data node names,
+        # as this may lead to appearing of Const ops with identical names.
+        self.assertFalse(conv_in_data_name == const_out_data_name)
+
+        # Attributes that are required for fusing are kept on data nodes.
+        # These checks are needed to ensure that _fuse_mul doesn't remove any of these attributes.
+        self.assertTrue(conv_in_data['output_channel_dim'] == 3)
+        self.assertTrue(conv_in_data['input_channel_dim'] == 2)
+        self.assertTrue(conv_in_data['dims_number'] == 4)
+        self.assertTrue(mul_node['can_be_fused'] is True)
+
 
 # Unit tests for fuse_linear_ops
 class FuseLinOpsTests(unittest.TestCase):