Extreme operating environments pose significant challenges for the next generation of aerospace materials. Traditional materials design methods, characterized by low efficiency, high costs, and long development cycles, have severely hindered the advancement of aerospace materials development. The development of new aerospace materials calls for innovative, highly efficient, and precise research and development paradigms. Artificial intelligence (AI) technologies, particularly the rapid advances in machine learning and deep learning, have emerged as powerful tools for aerospace materials research, markedly enhancing the efficiency of new material designs and the accuracy of performance predictions. This paper provides a systematic review of the research progress of AI in the aerospace materials field. It begins with an introduction to AI-assisted multiscale computational simulation and intelligent experimentation, then comprehensively presents surrogate model-accelerated materials optimization methods and a new materials design process centered on large-scale models. Detailed case studies are also presented on AI applications in the research and development of alloy materials, composite materials, and metamaterials. Finally, the paper summarizes the advantages and challenges of AI-assisted aerospace materials design and offers insights into future research directions.