Traffic congestion and incidents are major challenges globally, often resulting in economic losses and compromised safety. This study investigates machine learning algorithms, diverse datasets, and evaluation metrics for predicting traffic incidents. It highlights the integration of real-time analytics to enhance traffic management and discusses their implications for urban mobility and road safety. The research also evaluates the challenges in deploying machine learning-based systems and proposes solutions for future scalability and optimization.