Urban traffic congestion remains a critical challenge for smart city development, necessitating innovative approaches to improve traffic flow and reduce delays. This study presents a novel framework that integrates the Spatiotemporal Graph Convolutional Network-Long Short-Term Memory (STGCN-LSTM) model for traffic flow prediction with the Proximal Policy Optimization (PPO) algorithm for dynamic traffic signal control. The STGCN-LSTM model captures complex spatiotemporal dependencies, achieving an R2 of 0.904 on the METR-LA dataset. Extensive experiments and ablation studies highlight the complementary strengths of STGCN and LSTM, with the hybrid model outperforming standalone variants. The PPO algorithm dynamically adjusts signal timings, reducing vehicle waiting times by 30% and increasing traffic throughput by 15%. Incorporating external factors, such as weather and holidays, enhances the framework’s robustness in dynamic conditions, including adverse weather and traffic surges. GPU acceleration ensures scalability, enabling deployment in large-scale urban networks efficiently. This framework demonstrates significant potential to address urban congestion, reduce carbon emissions by 12%, and support sustainable urban development. Future research will explore edge computing, multi-agent reinforcement learning, and real-time data integration to further enhance scalability and adaptability.