The swift increase in urban populations has considerably put pressure on existing transportation systems, leading to more traffic congestion, longer travel times, and elevated pollution levels. Traditional traffic management strategies have proven insufficient in tackling the growing complexities of contemporary urban traffic. A cloud-based IoT platform provides effective solutions by facilitating the collection, processing, and analysis of real-time data to improve city traffic operations. This paper presents a hybrid cloud-edge framework for managing traffic in smart cities, combining the on-the-spot decision-making advantages of edge computing with the analytical and long-term planning strengths of cloud computing. To support this, IoT devices like intelligent streetlights, connected vehicles, and various sensors are strategically deployed throughout the city to collect traffic data. Edge computing processes this local data to quickly respond to changing traffic situations, while cloud platforms utilize machine learning algorithms for more comprehensive data analysis. Predictive models developed in the cloud anticipate traffic congestion in urban areas and adjust traffic signal timings accordingly. Results from tests show a 25% decrease in traffic incidents, a 15% drop in average travel times, and enhanced air quality. These results demonstrate that cloud-based IoT platforms can improve traffic flow in urban settings while reducing environmental impacts. This study emphasizes the transformative potential of cloud-driven IoT systems for managing urban traffic, promoting safer and more efficient smart cities.