diff --git a/kubernetes/kserve/kserve_wrapper/TorchserveModel.py b/kubernetes/kserve/kserve_wrapper/TorchserveModel.py
index 69c98b46cc..acf93a851d 100644
--- a/kubernetes/kserve/kserve_wrapper/TorchserveModel.py
+++ b/kubernetes/kserve/kserve_wrapper/TorchserveModel.py
@@ -1,13 +1,16 @@
 """ The torchserve side inference end-points request are handled to
     return a KServe side response """
 import logging
+import os
 import pathlib
+import time
 from enum import Enum
 from typing import Dict, Union
 
 import grpc
 import inference_pb2_grpc
 import kserve
+import requests
 from gprc_utils import from_ts_grpc, to_ts_grpc
 from inference_pb2 import PredictionResponse
 from kserve.errors import ModelMissingError
@@ -25,6 +28,7 @@
 EXPLAINER_URL_FORMAT = EXPLAINER_v2_URL_FORMAT = "http://{0}/explanations/{1}"
 REGISTER_URL_FORMAT = "{0}/models?initial_workers=1&url={1}"
 UNREGISTER_URL_FORMAT = "{0}/models/{1}"
+READINESS_URL_FORMAT = "{0}/models/{1}?customized={2}"
 
 
 class PredictorProtocol(Enum):
@@ -150,5 +154,60 @@ def load(self) -> bool:
         ]
         if len(existing_paths) == 0:
             raise ModelMissingError(model_path)
-        self.ready = True
+
+        num_try = 0
+        model_load_customized = os.environ.get("MODEL_LOAD_CUSTOMIZED", "false")
+        model_load_max_try = int(os.environ.get("MODEL_LOAD_MAX_TRY", 10))
+        model_load_delay = int(os.environ.get("MODEL_LOAD_DELAY", 30))
+        model_load_timeout = int(os.environ.get("MODEL_LOAD_TIMEOUT", 5))
+        while num_try < model_load_max_try and not self.ready:
+            num_try = num_try + 1
+            logging.info(
+                f"Loading {self.name} .. {num_try} of {model_load_max_try} tries.."
+            )
+
+            try:
+                response = requests.get(
+                    READINESS_URL_FORMAT.format(
+                        self.management_address, self.name, model_load_customized
+                    ),
+                    timeout=model_load_timeout,
+                ).json()
+
+                default_verison = response[0]
+
+                workers = default_verison["workers"]
+                workers_status = [
+                    worker["id"] for worker in workers if worker["status"] == "READY"
+                ]
+
+                worker_ready = False
+                if len(workers_status) > 0:
+                    worker_ready = True
+
+                self.ready = (
+                    worker_ready
+                    if model_load_customized == "false"
+                    else worker_ready and "customizedMetadata" in default_verison
+                )
+
+            except (
+                requests.ConnectionError,
+                requests.Timeout,
+                requests.ConnectTimeout,
+                requests.ReadTimeout,
+            ) as e:
+                logging.info(f"The model {self.name} is not ready")
+
+            except Exception as e:
+                logging.info(e)
+                logging.info(f"Failed loading model {self.name}")
+                break
+
+            logging.info(f"Sleep {model_load_delay} seconds for load {self.name}..")
+            time.sleep(model_load_delay)
+
+        if self.ready:
+            logging.info(f"The model {self.name} is ready")
+
         return self.ready
diff --git a/kubernetes/kserve/kserve_wrapper/__main__.py b/kubernetes/kserve/kserve_wrapper/__main__.py
index f67e6de107..eee4e76259 100644
--- a/kubernetes/kserve/kserve_wrapper/__main__.py
+++ b/kubernetes/kserve/kserve_wrapper/__main__.py
@@ -115,6 +115,12 @@ def parse_config():
         # By default model.load() is called on first request. Enabling load all
         # model in TS config.properties, all models are loaded at start and the
         # below method sets status to true for the models.
+        # However, even if all preparations related to loading the model (e.g.,
+        # download pretrained models using online storage) are not completed in
+        # torchserve handler, if model.ready=true is set, there may be problems.
+        # Therefore, the ready status is determined using the api provided by
+        # torchserve.
+
         model.load()
         models.append(model)