• Transfer learning effectively mitigates small-sample and heterogeneous data challenges in metallic materials property prediction.
• The review systematically categorizes transfer learning into feature-based, instance-based, parameter-based, and knowledge-based methods.
• Case studies demonstrate significant improvements in prediction accuracy, data efficiency, and interpretability for mechanical properties and alloy design.
• Emerging trends such as hybrid, multi-task, meta, and adaptive transfer learning, along with data standardization and physics integration, will drive future advances.