This thesis explores the development of an AI-driven system for improving traffic and transportation management. It applies machine learning and data analytics methods to predict traffic flow and prevent accidents. The research focuses on building accurate, scalable models that can analyze real-world data and support decision-making in urban mobility. By integrating artificial intelligence techniques with transportation datasets, the study demonstrates how automation and predictive modeling can enhance safety and efficiency. The results contribute to the growing field of intelligent transportation systems and highlight the potential of AI for solving modern traffic challenges.