diff --git a/keras/engine/data_adapter.py b/keras/engine/data_adapter.py
index 9201bfe3be0..517684e7559 100644
--- a/keras/engine/data_adapter.py
+++ b/keras/engine/data_adapter.py
@@ -1271,6 +1271,13 @@ def __init__(
         self._insufficient_data = False
         self._model = model
 
+        if steps_per_epoch == 0:
+            raise ValueError(
+                "Unexpected value for `steps_per_epoch`. Received value is 0. "
+                "Please check the docstring for `model.fit()` for supported "
+                "values."
+            )
+
         self._steps_per_epoch = steps_per_epoch
 
         # `steps_per_execution_value` is the cached initial value.
@@ -1308,6 +1315,9 @@ def __init__(
             strategy, x, steps_per_epoch, class_weight, distribute
         )
 
+        if self._inferred_steps == 0:
+            raise ValueError("Expected input data to be non-empty.")
+
     def _configure_dataset_and_inferred_steps(
         self, strategy, x, steps_per_epoch, class_weight, distribute
     ):
diff --git a/keras/engine/data_adapter_test.py b/keras/engine/data_adapter_test.py
index 5878e887f9b..2a480b385b9 100644
--- a/keras/engine/data_adapter_test.py
+++ b/keras/engine/data_adapter_test.py
@@ -1442,6 +1442,37 @@ def test_single_x_input_no_tuple_wrapping(self, use_numpy):
                 # Check that single x input is not wrapped in a tuple.
                 self.assertIsInstance(next(iterator), tf.Tensor)
 
+    def test_error_if_zero_steps_per_epoch(self):
+        data = tf.data.Dataset.from_tensor_slices([0, 1, 2, 3]).batch(1)
+
+        with self.assertRaisesRegex(
+            ValueError,
+            "Unexpected value for `steps_per_epoch`. Received value is 0.",
+        ):
+            data_adapter.DataHandler(
+                data, initial_epoch=0, epochs=2, steps_per_epoch=0
+            )
+
+    def test_error_if_empty_array_input_data(self):
+        x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
+        y = np.array([0, 1, 1, 0])
+        idx = []
+
+        with self.assertRaisesWithLiteralMatch(
+            ValueError,
+            "Expected input data to be non-empty.",
+        ):
+            data_adapter.DataHandler(x[idx], y[idx])
+
+    def test_error_if_empty_dataset_input_data(self):
+        data = tf.data.Dataset.from_tensor_slices([]).batch(1)
+
+        with self.assertRaisesWithLiteralMatch(
+            ValueError,
+            "Expected input data to be non-empty.",
+        ):
+            data_adapter.DataHandler(data)
+
 
 class TestValidationSplit(test_combinations.TestCase):
     @parameterized.named_parameters(("numpy_arrays", True), ("tensors", False))
diff --git a/keras/engine/training_test.py b/keras/engine/training_test.py
index 0c6dc9d66ad..ea040ac65b0 100644
--- a/keras/engine/training_test.py
+++ b/keras/engine/training_test.py
@@ -94,7 +94,7 @@ def test_fit_on_empty(self):
         model = sequential.Sequential([layers_module.Dense(1)])
         model.compile("sgd", "mse", run_eagerly=test_utils.should_run_eagerly())
         with self.assertRaisesRegex(
-            ValueError, "Unexpected result of `train_function`.*"
+            ValueError, "Expected input data to be non-empty."
         ):
             model.fit(x=np.array([]), y=np.array([]))
 
@@ -2534,7 +2534,7 @@ def test_predict_error_with_empty_x(self):
         model.compile(loss="mse")
 
         with self.assertRaisesRegex(
-            ValueError, "Unexpected result of `predict_function`.*"
+            ValueError, "Expected input data to be non-empty."
         ):
             model.predict(np.array([]))