Short-term demand forecasting, often defined as less than an hour into the future, is critical to implementing dynamic control strategies and providing useful customer information in transportation applications. By understanding expected demand, bus operators can deploy real-time control strategies before demand surges and minimize the impact of anomalies on service quality and passenger experience. One of the most useful applications of traffic demand forecasting models is to predict congestion and vehicle congestion at station platforms.This paper explores the integration of machine learning into urban rail transit systems to enhance efficiency, reliability, and sustainability. By leveraging machine learning paradigms, the paper examines how advanced data analytics can revolutionize passenger flow prediction, train operations, maintenance strategies, and system optimization. Ultimately, the goal is to propel urban rail transit into a new era of intelligent and resilient transportation, contributing to sustainable and livable cities.