This paper investigates the effectiveness of Particle Swarm Optimization (PSO), Grey Wolf Op-timization (GWO), and a Hybrid PSO-GWO method in optimizing traffic light control using IoT-enabled data from sensors. The study aims to enhance traffic movement, minimize delays, and improve overall traffic precision. Results demonstrate that the Hybrid PSO-GWO method out-performs both PSO and GWO individually. It . The hybrid approach leverages the strengths of both PSO and GWO, proving to be the most effective solution for smart traffic management. This re-search highlights the potential of hybrid optimization techniques and the critical role of IoT in developing intelligent traffic control systems.