In this research paper, an intelligent route selection strategy is proposed for optimizing transportation networks, inspired by Ant Colony Optimization (ACO). This approach aims to maximize overall system performance by considering factors such as route diversity, fuel consumption, trip duration, and environmental impact. ACO is known for its adaptive and decentralized nature, which allows it to adjust to changing conditions and dynamic network scenarios. The algorithm mimics the decentralized and adaptive foraging behaviour of ants, enabling it to constantly modify route choices based on current conditions through pheromone communication and iterative processes. By using this ACO-inspired algorithm, transportation networks can achieve resource-optimized, time-efficient, and cost-effective transportation. The research also proposes an adaptive approach for updating pheromones based on real-time congestion levels, allowing for dynamic and real-time modifications to the algorithm. One of the novel aspects of this research is the incorporation of a fuel consumption model in addition to conventional route optimization measures, providing a more comprehensive assessment of the algorithm's effectiveness in a transportation network. Additionally, the research utilizes parallel computing to improve simulation performance when dealing with a large number of vehicles and iterations. Overall, this research presents a promising approach to optimize transportation networks, offering insights into emerging mobility patterns and potentially improving overall system efficiency.