diff --git a/monai/inferers/utils.py b/monai/inferers/utils.py
index 766486a807..3dfbc2032c 100644
--- a/monai/inferers/utils.py
+++ b/monai/inferers/utils.py
@@ -76,7 +76,8 @@ def sliding_window_inference(
 
     Args:
         inputs: input image to be processed (assuming NCHW[D])
-        roi_size: the spatial window size for inferences.
+        roi_size: the spatial window size for inferences, this must be a single value or a tuple with values
+            for each spatial dimension (eg. 2 for 2D, 3 for 3D).
             When its components have None or non-positives, the corresponding inputs dimension will be used.
             if the components of the `roi_size` are non-positive values, the transform will use the
             corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted
@@ -131,11 +132,30 @@ def sliding_window_inference(
         kwargs: optional keyword args to be passed to ``predictor``.
 
     Note:
-        - input must be channel-first and have a batch dim, supports N-D sliding window.
+        - Inputs must be channel-first and have a batch dim (NCHW / NCDHW).
+        - If your data is NHWC/NDHWC, please apply `EnsureChannelFirst` / `EnsureChannelFirstd` upstream.
+
+    Raises:
+        ValueError: When the input dimensions do not match the expected dimensions based on ``roi_size``.
 
     """
-    buffered = buffer_steps is not None and buffer_steps > 0
     num_spatial_dims = len(inputs.shape) - 2
+
+    # Only perform strict shape validation if roi_size is a sequence (explicit dimensions).
+    # If roi_size is an integer, it is broadcast to all dimensions, so we cannot
+    # infer the expected dimensionality to enforce a strict check here.
+    if isinstance(roi_size, Sequence):
+        roi_dims = len(roi_size)
+        if num_spatial_dims != roi_dims:
+            raise ValueError(
+                f"Inputs must have {roi_dims + 2} dimensions for {roi_dims}D roi_size "
+                f"(Batch, Channel, {', '.join(['Spatial'] * roi_dims)}), "
+                f"but got inputs shape {inputs.shape}.\n"
+                "If you have channel-last data (e.g. B, D, H, W, C), please use "
+                "monai.transforms.EnsureChannelFirst or EnsureChannelFirstd upstream."
+            )
+    # -----------------------------------------------------------------
+    buffered = buffer_steps is not None and buffer_steps > 0
     if buffered:
         if buffer_dim < -num_spatial_dims or buffer_dim > num_spatial_dims:
             raise ValueError(f"buffer_dim must be in [{-num_spatial_dims}, {num_spatial_dims}], got {buffer_dim}.")
diff --git a/tests/inferers/test_sliding_window_inference.py b/tests/inferers/test_sliding_window_inference.py
index f97cbb9299..8700c4fcd0 100644
--- a/tests/inferers/test_sliding_window_inference.py
+++ b/tests/inferers/test_sliding_window_inference.py
@@ -372,6 +372,26 @@ def compute_dict(data):
         for rr, _ in zip(result_dict, expected_dict):
             np.testing.assert_allclose(result_dict[rr].cpu().numpy(), expected_dict[rr], rtol=1e-4)
 
+    def test_strict_shape_validation(self):
+        """Test strict shape validation to ensure inputs match roi_size dimensions."""
+        device = "cpu"
+        roi_size = (16, 16, 16)
+        sw_batch_size = 4
+
+        def predictor(data):
+            return data
+
+        # Case 1: Input has fewer dimensions than expected (e.g., missing Batch or Channel)
+        # 3D roi_size requires 5D input (B, C, D, H, W), giving 4D here.
+        inputs_4d = torch.randn((1, 16, 16, 16), device=device)
+        with self.assertRaisesRegex(ValueError, "Inputs must have 5 dimensions"):
+            sliding_window_inference(inputs_4d, roi_size, sw_batch_size, predictor)
+
+        # Case 2: Input is 3D (missing Batch AND Channel)
+        inputs_3d = torch.randn((16, 16, 16), device=device)
+        with self.assertRaisesRegex(ValueError, "Inputs must have 5 dimensions"):
+            sliding_window_inference(inputs_3d, roi_size, sw_batch_size, predictor)
+
 
 class TestSlidingWindowInferenceCond(unittest.TestCase):
     @parameterized.expand(TEST_CASES)