Urban centers face increasing traffic congestion, impacting economic efficiency, environmental sustainability, and overall quality of life. This paper explores the potential of AI-powered traffic control systems within IoT-based smart cities as a transformative approach to enhance urban mobility. These systems are designed to dynamically manage traffic flows, reduce congestion, and improve safety by integrating AI algorithms with IoT-generated real-time traffic data. Through an in-depth analysis of current applications and case studies, this paper demonstrates the effectiveness of AI-driven models in optimizing traffic signals, predicting traffic density, and adjusting urban transportation infrastructure accordingly. Our findings reveal significant improvements in travel efficiency, emission reduction, and road safety, highlighting the potential of AI and IoT in reshaping urban environments. Future work can address remaining challenges, such as scaling technology to megacities and integrating autonomous vehicles.