diff --git a/geo_deep_learning/train.py b/geo_deep_learning/train.py
index d2f63218..9f899662 100644
--- a/geo_deep_learning/train.py
+++ b/geo_deep_learning/train.py
@@ -1,6 +1,8 @@
 import torch
 from lightning.pytorch import Trainer
 from lightning.pytorch.cli import ArgsType, LightningCLI
+from lightning.pytorch.loggers import MLFlowLogger
+from lightning.pytorch.loggers import Logger
 
 
 class GeoDeepLearningCLI(LightningCLI):
@@ -16,7 +18,9 @@ def after_fit(self):
                                    accelerator="auto", 
                                    strategy="auto")
             best_model = self.model.__class__.load_from_checkpoint(best_model_path)
-            test_trainer.test(model=best_model, dataloaders=self.datamodule.test_dataloader())
+            test_results = test_trainer.test(model=best_model, dataloaders=self.datamodule.test_dataloader())
+            self.log_test_metrics_to_mlflow(test_results)
+            # self.log_test_metrics(test_results)
         self.trainer.strategy.barrier()
     
     def print_dataset_sizes(self):
@@ -28,6 +32,40 @@ def print_dataset_sizes(self):
             print(f"Number of training samples: {train_size}")
             print(f"Number of validation samples: {val_size}")
             print(f"Number of test samples: {test_size}")
+    
+    def log_test_metrics_to_mlflow(self, test_results):
+        # Get the MLflow logger from the trainer
+        mlf_logger = next((logger for logger in self.trainer.loggers if isinstance(logger, MLFlowLogger)), None)
+        
+        if mlf_logger is not None:
+            # Log each metric from the test results
+            for metric_name, metric_value in test_results[0].items():
+                mlf_logger.experiment.log_metric(
+                    run_id=mlf_logger.run_id,
+                    key=f"test_{metric_name}",
+                    value=metric_value
+                )
+            print("Test metrics logged to MLflow successfully.")
+        else:
+            print("MLflow logger not found in the trainer's loggers.")
+    
+    # def log_test_metrics(self, test_results):
+    #     if not self.trainer.logger:
+    #         print("No logger found. Test metrics will not be logged.")
+    #         return
+
+    #     if isinstance(self.trainer.logger, Logger):
+    #         for metric_name, metric_value in test_results[0].items():
+    #             self.trainer.logger.log_metrics({f"test_{metric_name}": metric_value})
+    #         print("Test metrics logged successfully.")
+    #     elif isinstance(self.trainer.logger, list):
+    #         for logger in self.trainer.logger:
+    #             if isinstance(logger, Logger):
+    #                 for metric_name, metric_value in test_results[0].items():
+    #                     logger.log_metrics({f"test_{metric_name}": metric_value})
+    #         print("Test metrics logged successfully to all loggers.")
+    #     else:
+    #         print("Unsupported logger type. Test metrics will not be logged.")
 
 
 def main(args: ArgsType = None) -> None: