With the acceleration of urbanization and the continuous expansion of rail transit networks, rail transit systems face the demand for more efficient, refined, and intelligent solutions in operation scheduling, facility maintenance, and train control. Artificial intelligence (AI), with its advantages in data perception, pattern recognition, and intelligent decision-making, is profoundly reshaping the technical system and management mode of rail transit. From the core perspective of AI-empowering rail transit, this paper systematically reviews the current research progress and engineering practices around the three key links of operation optimization, equipment maintenance, and intelligent driving, and summarizes the main challenges of existing methods in terms of high-dimensional data, insufficient model interpretability, and limited system integration. Furthermore, the coping paths of AI technologies such as deep learning, graph neural networks, collaborative multi-agent reinforcement learning, self-supervised learning, and digital twins are discussed. Finally, the trends of future rail transit in the directions of computing power support, standard system construction, and human-machine collaborative agent development are prospected, aiming to provide theoretical reference and practical enlightenment for the deep integration of AI and rail transit.