diff --git a/src/sempy_labs/_model_bpa_bulk.py b/src/sempy_labs/_model_bpa_bulk.py
index 70a8a5dd..49377639 100644
--- a/src/sempy_labs/_model_bpa_bulk.py
+++ b/src/sempy_labs/_model_bpa_bulk.py
@@ -28,6 +28,7 @@ def run_model_bpa_bulk(
     workspace: Optional[str | UUID | List[str | UUID]] = None,
     skip_models: Optional[str | List[str]] = ["ModelBPA", "Fabric Capacity Metrics"],
     skip_models_in_workspace: Optional[dict] = None,
+    return_df: bool = False,
 ):
     """
     Runs the semantic model Best Practice Analyzer across all semantic models in a workspace (or all accessible workspaces).
@@ -54,6 +55,7 @@ def run_model_bpa_bulk(
             "Workspace A": ["Dataset1", "Dataset2"],
             "Workspace B": ["Dataset5", "Dataset 8"],
         }
+    return_df: bool = False (default: False) – If set to True, it will return the BPA rules as a DataFrame for the specified workspaces.`
     """
 
     if not lakehouse_attached():
@@ -66,6 +68,9 @@ def run_model_bpa_bulk(
 
     skip_models.extend(["ModelBPA", "Fabric Capacity Metrics"])
 
+    #Declaring a tempDf to return the BPARules
+    tempDf = pd.DataFrame()
+
     now = datetime.datetime.now()
     output_table = "modelbparesults"
     lakeT = get_lakehouse_tables()
@@ -175,6 +180,9 @@ def run_model_bpa_bulk(
                         for key, value in icons.bpa_schema.items()
                     }
 
+                    #Appending all the dfs to tempDf    
+                    tempDf = pd.concat([tempDf, df], ignore_index=True)
+
                     save_as_delta_table(
                         dataframe=df,
                         delta_table_name=output_table,
@@ -185,6 +193,9 @@ def run_model_bpa_bulk(
                     print(
                         f"{icons.green_dot} Saved BPA results to the '{output_table}' delta table."
                     )
+    #If `return_df` is set to `True`, it will return the BPA rules result as a DataFrame.
+    if(return_df):
+        return tempDf
 
     print(f"{icons.green_dot} Bulk BPA scan complete.")
 
@@ -227,6 +238,9 @@ def create_model_bpa_semantic_model(
         lakehouse=lakehouse, workspace=lakehouse_workspace_id
     )
 
+    
+
+
     # Generate the shared expression based on the lakehouse and lakehouse workspace
     expr = generate_shared_expression(
         item_name=lakehouse_name,