diff --git a/keras/src/trainers/data_adapters/py_dataset_adapter.py b/keras/src/trainers/data_adapters/py_dataset_adapter.py
index 18865af026cf..88177a39eca3 100644
--- a/keras/src/trainers/data_adapters/py_dataset_adapter.py
+++ b/keras/src/trainers/data_adapters/py_dataset_adapter.py
@@ -40,6 +40,12 @@ class PyDataset:
             multiprocessed setting.
             Reduce this value to reduce the CPU memory consumption of
             your dataset. Defaults to 10.
+        shuffle: Whether to shuffle the sample ordering at the end of
+            each epoch. This argument is passed to `model.fit()`. When
+            calling `model.fit(..., shuffle=True)`, the training loop
+            automatically calls `on_epoch_end()` at each epoch
+            boundary, allowing datasets to implement custom
+            shuffling logic. Defaults to `False`.
 
     Notes:
 
@@ -52,6 +58,9 @@ class PyDataset:
         over the dataset. They are not being used by the `PyDataset` class
         directly. When you are manually iterating over a `PyDataset`,
         no parallelism is applied.
+    - `shuffle=False` keeps the sample order fixed across epochs.
+        For distributed or deterministic training prefer
+        `shuffle=False` and manage the order externally.
 
     Example:
 
@@ -66,10 +75,17 @@ class PyDataset:
 
     class CIFAR10PyDataset(keras.utils.PyDataset):
 
-        def __init__(self, x_set, y_set, batch_size, **kwargs):
+        def __init__(self, x_set, y_set, batch_size, shuffle=False, **kwargs):
             super().__init__(**kwargs)
             self.x, self.y = x_set, y_set
             self.batch_size = batch_size
+            self.shuffle = shuffle
+            # create index array for shuffling
+            self.indices = np.arange(len(self.x))
+            # Shuffle once at initialization when shuffle=True
+            if self.shuffle:
+                np.random.shuffle(self.indices)
+
 
         def __len__(self):
             # Return number of batches.
@@ -81,12 +97,19 @@ def __getitem__(self, idx):
             # Cap upper bound at array length; the last batch may be smaller
             # if the total number of items is not a multiple of batch size.
             high = min(low + self.batch_size, len(self.x))
-            batch_x = self.x[low:high]
-            batch_y = self.y[low:high]
+            # Retrieve a batch using shuffled indices
+            batch_indices = self.indices[low:high]
+            batch_x = self.x[batch_indices]
+            batch_y = self.y[batch_indices]
 
             return np.array([
                 resize(imread(file_name), (200, 200))
                    for file_name in batch_x]), np.array(batch_y)
+
+        def on_epoch_end(self):
+            # Shuffle indices at the end of each epoch if enabled
+            if self.shuffle:
+                np.random.shuffle(self.indices)
     ```
     """