Asa strategic and fundamental industry, new materials represent a crucial industrial direction for accelerating the development of new-quality products and steadily promoting high-quality development. Materials genome engineering (MGE) deeply integrates computational simulation, high-throughput experiments, and data science, significantly enhancing the R&D efficiency of new materials. Its material databases and cross-scale models are becoming the technological foundation for the in-depth application of artificial intelligence in material design, performance prediction, and other aspects. With the rapid development of artificial intelligence technology, the combination of MGE and intelligent science is facing unprecedented opportunities and challenges. This paper reviews the background and history of the emergence of artificial intelligence in materials in the AI+ era, as well as its material data infrastructure and the AI technologies used. It summarizes the applications of technologies such as machine learning and natural language processing in material reverse design and screening, physical property prediction and characterization analysis, and performance optimization. It also introduces the paradigm innovation of autonomous laboratory systems. Finally, this paper looks ahead to the potential challenges that AI may face in the field of materials science and proposes directions and suggestions for future improvement.