Increased vehicle usage resulting from accelerating population growth and ease of access poses significant challenges to modern traffic management systems. This research article explores the amazing capabilities of Deep Learning traffic prediction models by suggesting a unique hybrid traffic management framework that depends on Convolutional Neural Networks and Long Short-Term Memory Networks to determine space and time-varying attributes respectively, finding joint spatial–temporal correlations in historical traffic flow data. Efficient hyperparameter tuning is ensured through Particle Swarm Optimization and Bayesian Optimization. Real-time decision-making is facilitated through the integration of Deep Reinforcement Learning, thus seamlessly adapting to ever-changing traffic conditions and mitigating vehicular congestion, offering promising applications in urban traffic management systems.