In recent years, the popularity of vehicle adhoc networks (VANET) in wireless intelligent transportation systems has significantly increased. It is difficult to determine the quickest path between the source and the target in a VANET traffic system. Longer routes feature increased network overhead, more expensive connections, more path failures, and worse routing efficiency. Identifying the shortest route in optimization, often known as the Travelling Salesman Problem (TSP) and traffic congestion, is a well-known combinatorial optimization challenge with various practical applications. To improve routing efficiency, the HACOSMO (Hybrid-Ant Colony Optimization with Spider Monkey Optimization) system’s recommended meta-heuristic approach finds the shortest path using distance and traffic based principles. The simulation (NS-2) outcomes focused on the efficiency of the proposed method beats the other existing methods like ACO, GRACO, OACO, and IDBACOR in terms of overhead, throughput, latency, packet failure, and message transmission ratio. According to HACOSMO, routing overhead is 8% to 11% under IDBACOR, 10% to 14% under GRACO,19% to 31% under OACO, and 24% to 37% under ACO for a variety of vehicle counts and speed ranges. When compared to IDBACOR, the proposed method enhances throughput by 3% to 7%, 6% to 8% when compared to GRACO, 11% to 15% when compared to OACO, and 16% to 25% when compared to ACO.