This study explores the optimization of shortest paths in intelligent transportation systems using a hybrid Particle Swarm Optimization and Ant Colony Optimization (PSOACO) algorithm. The study focuses on improving traffic flow efficiency and minimizing travel time by finding the most optimal routes for vehicles. Utilizing simulations and algorithmic analysis, the PSOACO algorithm is evaluated for its effectiveness in guiding vehicles through complex traffic networks. The results demonstrate significant improvements in route planning and traffic coordination, showcasing the potential of hybrid optimization techniques in enhancing intelligent transportation systems' performance. This study contributes to the advancement of efficient routing strategies, critical for managing traffic congestion and improving overall transportation system efficiency.