Urban traffic congestion is a growing problem in cities, leading to notable delays, increased fuel consumption, and elevated air pollution levels. Effective traffic management is crucial for enhancing urban mobility and improving residents' quality of life. This paper presents a novel Internet of Things (IoT)-based predictive analytics framework that tackles challenges in traffic management. The method employs IoT sensors spread throughout the city, including real-time traffic cameras, vehicle counting equipment, and environmental monitors, to gather comprehensive data on traffic flow, speed, and density. We applied advanced machine learning techniques, particularly time series analysis and regression methods, to analyze the collected data and forecast future traffic conditions. Our model can pinpoint potential congestion hotspots by examining historical traffic trends in conjunction with real-time data and suggest optimal adjustments for traffic signals ahead of time. Testing our predictive analytics framework in a selected urban area showed an impressive 30% decrease in peak-hour congestion and a 20% enhancement in overall traffic flow. Furthermore, the analysis demonstrated a 15% reduction in average vehicle emissions throughout the trial period, underscoring the environmental advantages of the system. These results suggest that utilizing IoT technology alongside predictive analytics can enhance traffic management and support sustainable urban growth. By equipping city planners and traffic management agencies with practical insights, our research aids in the advancement of smarter cities capable of addressing the complexities of contemporary transportation issues. The findings of this study emphasize the possibility for wider implementation of IoT-driven solutions in urban planning, ultimately resulting in improved public safety, decreased environmental impact, and a better quality of life in urban areas.