Efficient traffic management is a major focus in smart city projects. Intelligent Transportation Systems (ITS) are revolutionize the way transportation networks are managed, to improve safety, efficiency, and sustainability. Machine learning (ML) has been developed as a powerful means for advancing ITS by enabling data-driven decision-making, real-time traffic management, predictive analytics, and automation. This paper offers a thorough summary of the current trends and advancements in the utilization of ML techniques in ITS. It explores key areas such as traffic flow prediction, autonomous vehicles, congestion management, smart mobility solutions, and road anomaly detection.