Abstract: Additive manufacturing (AM) has brought revolutionary opportunities for the integrated forming and lightweight design of complex metal components in the aerospace field. This technology is highly aligned with the urgent demands for complex lightweight structures, integrated functional components, and rapid iterative development. Data-driven technologies, represented by machine learning (ML), provide powerful enabling capabilities for addressing challenges in quality control, process optimization, and innovative design within aerospace additive manufacturing. In this study, the technical pathways of machine learning in online monitoring and process parameter optimization for laser additive manufacturing are systematically reviewed, the cutting-edge research on the prediction of metallurgical defects and assessment of mechanical performance are delved into, and the groundbreaking applications of machine learning in the development of multi-component alloys and the optimization design of geometric structures for aerospace applications are set forth.