diff --git a/evadb/executor/drop_object_executor.py b/evadb/executor/drop_object_executor.py
index c4f108052..d27012328 100644
--- a/evadb/executor/drop_object_executor.py
+++ b/evadb/executor/drop_object_executor.py
@@ -13,9 +13,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+import shutil
 
 import pandas as pd
 
+from evadb.configuration.constants import TRAINING_FRAMEWORKS
 from evadb.database import EvaDBDatabase
 from evadb.executor.abstract_executor import AbstractExecutor
 from evadb.executor.executor_utils import ExecutorError, handle_vector_store_params
@@ -24,6 +27,7 @@
 from evadb.plan_nodes.drop_object_plan import DropObjectPlan
 from evadb.storage.storage_engine import StorageEngine
 from evadb.third_party.vector_stores.utils import VectorStoreFactory
+from evadb.utils.generic_utils import string_comparison_case_insensitive
 from evadb.utils.logging_manager import logger
 
 
@@ -94,19 +98,58 @@ def _handle_drop_function(self, function_name: str, if_exists: bool):
             function_entry = self.catalog().get_function_catalog_entry_by_name(
                 function_name
             )
-            for cache in function_entry.dep_caches:
-                self.catalog().drop_function_cache_catalog_entry(cache)
-
-            # todo also delete the indexes associated with the table
-
-            self.catalog().delete_function_catalog_entry_by_name(function_name)
-
-            return Batch(
-                pd.DataFrame(
-                    {f"Function {function_name} successfully dropped"},
-                    index=[0],
+        # training framework model cleanup on drop function
+        err_msg = (
+            f"Error removing {function_entry.type} model for function {function_name}."
+        )
+        try:
+            if function_entry.type.lower() in [x.lower() for x in TRAINING_FRAMEWORKS]:
+                filtered_metadata = list(
+                    filter(lambda x: x.key == "model_path", function_entry.metadata)
                 )
+                if len(filtered_metadata) > 0:
+                    model_path = os.path.abspath(filtered_metadata[0].value)
+                    """For 'Forecasting' the entire function catalog of forecasting functions
+                    is checked to see if the model path is shared"""
+                    if string_comparison_case_insensitive(
+                        function_entry.type, "Forecasting"
+                    ):
+                        forecasting_function_entries = (
+                            self.catalog().get_function_catalog_entries_by_type(
+                                function_entry.type
+                            )
+                        )
+                        functions_using_same_model = sum(
+                            1
+                            for entry in forecasting_function_entries
+                            if any(
+                                x.key == "model_path"
+                                and os.path.abspath(x.value) == model_path
+                                for x in entry.metadata
+                            )
+                        )
+                        if functions_using_same_model == 1:
+                            dir_path = os.path.abspath(os.path.dirname(model_path))
+                            if os.path.exists(dir_path):
+                                shutil.rmtree(dir_path)
+                    else:
+                        if os.path.exists(model_path):
+                            os.remove(model_path)
+
+        except Exception as e:
+            raise RuntimeError(f"{err_msg}\n{e}")
+
+        for cache in function_entry.dep_caches:
+            self.catalog().drop_function_cache_catalog_entry(cache)
+
+        # todo also delete the indexes associated with the table
+        self.catalog().delete_function_catalog_entry_by_name(function_name)
+        return Batch(
+            pd.DataFrame(
+                {f"Function {function_name} successfully dropped"},
+                index=[0],
             )
+        )
 
     def _handle_drop_index(self, index_name: str, if_exists: bool):
         index_obj = self.catalog().get_index_catalog_entry_by_name(index_name)