Abstract: Mineral prospectivity mapping (MPM) driven by big data and artificial intelligence (AI) represents a cutting-edge approach in mineral exploration. However, traditional methods typically suffer from limited generalization ability, poor transferability, and insufficient interpretability, resulting in the inability to achieve stable cross-regional applications. Foundation models, based on the “pretrain and fine-tune” paradigm, have demonstrated excellent cross-task transfer and strong generalization ability in the fields such as natural language processing and computer vision, offering a promising path to overcome the aforementioned bottlenecks. The development of foundation models for MPM holds significant potential to revolutionize traditional models and improve exploration efficiency, representing a new research direction for intelligent MPM. This study systematically reviews the state-of-the-art of the development and construction processes of foundation models, focusing on the technical characteristics of large language models, visual foundation models, and multimodal foundation models. This study also summarized the limitations of existing foundation models for MPM, and explored the construction process of MPM foundation models from a perspective of language-based, visual-based and multimodal-based foundation models, and discussed the challenges of developing MPM foundation models, providing a reference for development of MPM foundation models.