Abstract: In the era of materials genome engineering, data-driven machine learning has become a powerful tool for accelerating the research and development of metallic materials. However, the predictive accuracy and generalization ability of traditional machine learning models are often limited by the scarcity and heterogeneity of available data, especially in small-sample scenarios. To address these challenges, transfer learning has emerged as an effective strategy to leverage knowledge from related domains, thereby enhancing model performance with limited target data. This review systematically summarizes the fundamental concepts, methodologies, and representative applications of transfer learning in the prediction of metallic materials’ properties. Transfer learning can be categorized into feature-based, instance-based, parameter-based, and knowledge-based methods. This work discusses their respective mechanisms, advantages, and limitations. Case studies demonstrate that transfer learning can significantly improve prediction accuracy, data efficiency, and model interpretability in tasks such as mechanical property prediction and alloy design. Furthermore, this work highlights emerging trends including hybrid, multi-task, meta, and adaptive transfer learning, which further expand the applicability of these techniques. Finally, this work outlines future research directions, emphasizing the need for data standardization, algorithmic innovation, multimodal data fusion, and the integration of physical principles to achieve robust, interpretable, and generalizable models. The perspectives presented aim to advance the intelligent design and discovery of metallic materials, promoting efficient knowledge transfer and collaborative innovation in materials science.