Artificial intelligence (AI) is profoundly transforming the paradigms and methodologies of advanced materials research and development. This review systematically examines cutting−edge advances in AI applications across materials composition/structure design, property prediction, synthesis optimization, and industrial implementation. By integrating data−driven approaches, physics−informed modeling, and autonomous experimental systems, AI has enabled high−accuracy cross−scale performance prediction, inverse design of materials with extreme properties, intelligent optimization of synthesis processes, and non−destructive defect detection, significantly accelerating development cycles while overcoming performance bottlenecks. The work highlights breakthroughs in representative case studies including high−throughput screening of stable crystals, targeted development of radiative cooling materials, and optimization of electrolytes for high−voltage batteries, while elucidating how techniques such as few−shot learning, transfer learning, and physics−constrained algorithms address challenges in data scarcity and multiscale modeling. Looking forward, the synergistic convergence of AI with quantum computing and generative design will propel materials innovation toward an accelerated transition to advanced paradigms characterized by data−driven workflows, autonomous decision−making, and intelligent iteration.