Severe traffic congestion is a significant challenge for urban areas, and improving sustainable urban development is critical, yet traditional traffic management systems often struggle to cope with dynamic real-time conditions due to their reliance on predetermined schedules and fixed control mechanisms. This paper advocates for the application of optimizing techniques, specifically an enhanced version of ant colony optimization (ACO), to alleviate this challenge. By effectively managing and enhancing vehicle movement, these approaches target the reduction of congestion, travel times, and costs while concurrently enhancing fuel efficiency. This approach can also be adapted to optimize the deployment and movement of drones in wireless communication networks, ensuring optimal coverage and resource utilization. Implementations, comparisons, and visualizations show how these approaches help improve traffic movement, thereby minimizing congestion-associated problems.