diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py
index 3abe6db..5ae05aa 100644
--- a/deepface/DeepFace.py
+++ b/deepface/DeepFace.py
@@ -174,7 +174,7 @@ def analyze(
     expand_percentage: int = 0,
     silent: bool = False,
     anti_spoofing: bool = False,
-) -> List[Dict[str, Any]]:
+) -> Union[List[Dict[str, Any]], List[List[Dict[str, Any]]]]:
     """
     Analyze facial attributes such as age, gender, emotion, and race in the provided image.
     Args:
@@ -206,7 +206,10 @@ def analyze(
         anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
 
     Returns:
-        results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary represents
+        (List[List[Dict[str, Any]]]): A list of analysis results if received batched image, 
+                                      explained below.
+                                      
+        (List[Dict[str, Any]]): A list of dictionaries, where each dictionary represents
            the analysis results for a detected face. Each dictionary in the list contains the
            following keys:
 
@@ -253,6 +256,29 @@ def analyze(
             - 'middle eastern': Confidence score for Middle Eastern ethnicity.
             - 'white': Confidence score for White ethnicity.
     """
+
+    if isinstance(img_path, np.ndarray) and len(img_path.shape) == 4:
+        # Received 4-D array, which means image batch.
+        # Check batch dimension and process each image separately.
+        if img_path.shape[0] > 1:
+            batch_resp_obj = []
+            # Execute analysis for each image in the batch.
+            for single_img in img_path:
+                resp_obj = demography.analyze(
+                    img_path=single_img,
+                    actions=actions,
+                    enforce_detection=enforce_detection,
+                    detector_backend=detector_backend,
+                    align=align,
+                    expand_percentage=expand_percentage,
+                    silent=silent,
+                    anti_spoofing=anti_spoofing,
+                )
+
+                # Append the response object to the batch response list.
+                batch_resp_obj.append(resp_obj)
+            return batch_resp_obj
+
     return demography.analyze(
         img_path=img_path,
         actions=actions,