Road accidents and traffic congestion pose significant challenges to urban mobility, with conventional traffic management systems often failing to provide timely accident detection and emergency response. This study presents an IoT-enabled smart traffic control framework integrated with deep learning algorithms to achieve accurate, real-time accident identification and response. The system utilizes sensor networks, GPS data, and security camera feeds to detect and differentiate accidents from normal traffic events, ensuring rapid and automated crisis management. GPS technology plays a vital role by monitoring vehicle speeds, abrupt stops, and impact points, while edge computing, cloud-based analytics, and vehicle-to-infrastructure (V2I) communication enhance traffic flow optimization and emergency coordination. Case studies and comparative evaluations are conducted to demonstrate system effectiveness and explore future improvements, including 5G-enabled vehicle networks, AI-driven predictive analytics, and blockchain-based accident verification for secure data sharing. The integration of AI-powered image analysis and real-time sensor fusion enables high detection accuracy and reliability. Additionally, intelligent traffic management systems support proactive risk assessment, congestion prediction, and automatic rerouting, improving road safety and reducing fatalities. The proposed framework highlights the potential of combining IoT, deep learning, and smart infrastructure to develop scalable and efficient solutions for accident detection and traffic management in next-generation intelligent transportation networks.