diff --git a/src/otx/engine/engine.py b/src/otx/engine/engine.py
index 033b7ab2870..48daf0b760b 100644
--- a/src/otx/engine/engine.py
+++ b/src/otx/engine/engine.py
@@ -379,13 +379,7 @@ def predict(
 
         model = self.model
 
-        if checkpoint is not None:
-            checkpoint = str(checkpoint)
-        elif self.checkpoint is not None:
-            checkpoint = str(self.checkpoint)
-        else:
-            checkpoint = None
-
+        checkpoint = checkpoint if checkpoint is not None else self.checkpoint
         datamodule = datamodule if datamodule is not None else self.datamodule
 
         is_ir_ckpt = checkpoint is not None and Path(checkpoint).suffix in [".xml", ".onnx"]
@@ -400,8 +394,6 @@ def predict(
         )
         lit_module.label_info = datamodule.label_info
 
-        # NOTE, trainer.test takes only lightning based checkpoint.
-        # So, it can't take the OTX1.x checkpoint.
         if checkpoint is not None and not is_ir_ckpt:
             loaded_checkpoint = torch.load(checkpoint)
             lit_module.load_state_dict(loaded_checkpoint)
@@ -595,19 +587,27 @@ def explain(
         """
         from otx.algo.utils.xai_utils import dump_saliency_maps, process_saliency_maps_in_pred_entity
 
-        ckpt_path = str(checkpoint) if checkpoint is not None else self.checkpoint
-        if explain_config is None:
-            explain_config = ExplainConfig()
+        model = self.model
+
+        checkpoint = checkpoint if checkpoint is not None else self.checkpoint
+        datamodule = datamodule if datamodule is not None else self.datamodule
+
+        is_ir_ckpt = checkpoint is not None and Path(checkpoint).suffix in [".xml", ".onnx"]
+        if is_ir_ckpt and not isinstance(model, OVModel):
+            datamodule = self._auto_configurator.get_ov_datamodule()
+            model = self._auto_configurator.get_ov_model(model_name=str(checkpoint), label_info=datamodule.label_info)
 
         lit_module = self._build_lightning_module(
-            model=self.model,
+            model=model,
             optimizer=self.optimizer,
             scheduler=self.scheduler,
         )
-        if datamodule is None:
-            datamodule = self.datamodule
         lit_module.label_info = datamodule.label_info
 
+        if checkpoint is not None and not is_ir_ckpt:
+            loaded_checkpoint = torch.load(checkpoint)
+            lit_module.load_state_dict(loaded_checkpoint)
+
         lit_module.model.explain_mode = True
 
         self._build_trainer(**kwargs)
@@ -615,9 +615,11 @@ def explain(
         predict_result = self.trainer.predict(
             model=lit_module,
             datamodule=datamodule,
-            ckpt_path=ckpt_path,
         )
 
+        if explain_config is None:
+            explain_config = ExplainConfig()
+
         predict_result = process_saliency_maps_in_pred_entity(predict_result, explain_config)
         if dump:
             dump_saliency_maps(
diff --git a/tests/integration/api/test_engine_api.py b/tests/integration/api/test_engine_api.py
index 202e4097042..b26a297e6c2 100644
--- a/tests/integration/api/test_engine_api.py
+++ b/tests/integration/api/test_engine_api.py
@@ -37,6 +37,8 @@ def test_engine_from_config(
         pytest.skip(
             reason="H-labels require num_multiclass_head, num_multilabel_classes, which skip until we have the ability to automate this.",
         )
+    if "anomaly" in task.lower():
+        pytest.skip(reason="There's no dataset for anomaly tasks.")
 
     tmp_path_train = tmp_path / task
     engine = Engine.from_config(
@@ -86,6 +88,37 @@ def test_engine_from_config(
             test_metric_from_ov_model = engine.test(checkpoint=exported_model_path, accelerator="cpu")
         assert len(test_metric_from_ov_model) > 0
 
+    # List of models with explain supported.
+    if task not in [
+        OTXTaskType.MULTI_CLASS_CLS,
+        OTXTaskType.MULTI_LABEL_CLS,
+        # Will be supported after merging PR#2997
+        # OTXTaskType.DETECTION,
+        # OTXTaskType.ROTATED_DETECTION,
+        # OTXTaskType.INSTANCE_SEGMENTATION,
+    ]:
+        return
+
+    # Predict Torch model with explain
+    predictions = engine.predict(explain=True)
+    assert len(predictions[0].saliency_maps) > 0
+
+    # Export IR model with explain
+    exported_model_with_explain = engine.export(explain=True)
+    assert exported_model_with_explain.exists()
+
+    # Infer IR Model with explain: predict
+    predictions = engine.predict(explain=True, checkpoint=exported_model_with_explain, accelerator="cpu")
+    assert len(predictions) > 0
+    sal_maps_from_prediction = predictions[0].saliency_maps
+    assert len(sal_maps_from_prediction) > 0
+
+    # Infer IR Model with explain: explain
+    explain_results = engine.explain(checkpoint=exported_model_with_explain, accelerator="cpu")
+    assert len(explain_results[0].saliency_maps) > 0
+    sal_maps_from_explain = explain_results[0].saliency_maps
+    assert (sal_maps_from_prediction[0][0] == sal_maps_from_explain[0][0]).all()
+
 
 @pytest.mark.parametrize("recipe", pytest.TILE_RECIPE_LIST)
 def test_engine_from_tile_recipe(