With the rapid development of artificial intelligence (AI), its application in the field of nuclear fuel and materials is gradually becoming a new driving force for the advancement of nuclear energy technology. This article comprehensively reviews the current state of AI research in the field of nuclear fuel and materials and conducts an in-depth analysis of future development trends. It first introduces AI methods applied to scientific research, discussing from two aspects: network architecture and learning paradigms. Next it systematically summarizes the current state of AI applications in performance prediction at the material and structure levels of nuclear fuel materials, design optimization of materials and structures, and computer vision in fuel production and operation. The review then looks forward to the future development trends of the combination of AI with nuclear fuel and materials. At the algorithm level, it discusses methods to enhance the interpretability of machine learning models, quantify uncertainty, and the importance of limited supervised learning technology in reducing data requirements. At the application level, it discusses key technologies such as acceleration of multi-scale multi-physics field simulations, topological optimization and generative design, universal pre-trained models for nuclear material properties, and automated laboratories. Finally, several suggestions are proposed to further promote the application of AI in the field of nuclear fuel and materials.