Transferring knowledge across domains with heterogeneous feature spaces and modalities—such as text, vision, and speech—remains a fundamental challenge in machine learning. Traditional transfer learning methods often assume aligned feature representations between source and target, which limits their effectiveness in real-world scenarios involving cross-modal or structurally dissimilar domains. In this work, we propose a unified and interpretable framework that enables transfer learning through feature correspondences derived from model explanations. Our approach constructs transformation matrices between source and target feature spaces by leveraging feature relevance scores obtained from three interpretability techniques: Random Forest-based feature importance, SHAP (SHapley Additive exPlanations), and LIME (Local Interpretable Model-agnostic Explanations). This correspondence-driven alignment allows us to project source data into the target feature space, enabling effective knowledge transfer without requiring domain alignment or adversarial training. We conduct comprehensive experiments on three diverse datasets spanning text, vision, and audio. We conduct comprehensive experiments on three diverse datasets spanning text, vision, and audio. Results show up to 3–5% improvement in target domain accuracy over direct transfer baselines across all modalities, while also providing interpretable feature alignment.