diff --git a/tests/layer_tests/tensorflow_tests/test_tf_DivNoNan.py b/tests/layer_tests/tensorflow_tests/test_tf_DivNoNan.py
index 5a986a9ea6244f..357a0a962be8c0 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_DivNoNan.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_DivNoNan.py
@@ -1,12 +1,13 @@
 # Copyright (C) 2018-2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import platform
-
 import numpy as np
 import pytest
 import tensorflow as tf
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import mix_array_with_value
+
+rng = np.random.default_rng(23235)
 
 
 class TestDivNoNan(CommonTFLayerTest):
@@ -16,37 +17,34 @@ def _prepare_input(self, inputs_info):
         x_shape = inputs_info['x:0']
         y_shape = inputs_info['y:0']
         inputs_data = {}
-        inputs_data['x:0'] = np.random.randint(-10, 10, x_shape).astype(self.input_type)
-        # generate y in way to have zeros
-        inputs_data['y:0'] = np.random.randint(-10, 10, y_shape).astype(self.input_type) * \
-                           np.random.randint(0, 2, y_shape).astype(self.input_type)
+        inputs_data['x:0'] = rng.uniform(-5.0, 5.0, x_shape).astype(self.input_type)
+        # provide zeros in y input
+        y_data = rng.uniform(-5.0, 5.0, y_shape).astype(self.input_type)
+        y_data = mix_array_with_value(y_data, 0.0)
+        inputs_data['y:0'] = y_data
         return inputs_data
 
-    def create_div_no_nan_net(self, input_shape, input_type):
+    def create_div_no_nan_net(self, x_shape, y_shape, input_type):
         self.input_type = input_type
         tf.compat.v1.reset_default_graph()
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
-            x = tf.compat.v1.placeholder(input_type, input_shape, 'x')
-            y = tf.compat.v1.placeholder(input_type, input_shape, 'y')
+            x = tf.compat.v1.placeholder(input_type, x_shape, 'x')
+            y = tf.compat.v1.placeholder(input_type, y_shape, 'y')
             tf.raw_ops.DivNoNan(x=x, y=y)
             tf.compat.v1.global_variables_initializer()
             tf_net = sess.graph_def
 
         return tf_net, None
 
-    test_data_basic = [
-        dict(input_shape=[10, 20], input_type=np.float32),
-        dict(input_shape=[2, 3, 4], input_type=np.float32),
-    ]
-
-    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.parametrize('x_shape', [[], [4], [3, 4], [2, 3, 4]])
+    @pytest.mark.parametrize('y_shape', [[2, 3, 4]])
+    @pytest.mark.parametrize('input_type', [np.float16, np.float32, np.float64])
     @pytest.mark.precommit
     @pytest.mark.nightly
-    @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
-                       reason='Ticket - 122716')
-    def test_div_no_nan_basic(self, params, ie_device, precision, ir_version, temp_dir,
-                              use_legacy_frontend):
-        self._test(*self.create_div_no_nan_net(**params),
+    def test_div_no_nan_basic(self, x_shape, y_shape, input_type,
+                              ie_device, precision, ir_version,
+                              temp_dir, use_legacy_frontend):
+        self._test(*self.create_div_no_nan_net(x_shape, y_shape, input_type),
                    ie_device, precision, ir_version, temp_dir=temp_dir,
                    use_legacy_frontend=use_legacy_frontend)