<p>Traffic congestion in Vellore has gotten worse, making it harder to go about daily tasks, consuming more fuel, and polluting the environment. This study proposes a Multi-Parameter Dynamic Shortest Route Search Algorithm, which extends the Shortest Route* Search Algorithm introduced by Lakshna et al. (2023) through the incorporation of dynamic multi-parameter edge-cost updating using historical and real-time traffic information.The model combines real-time and historical traffic data, accounting for critical factors such as road types, vehicle characteristics, driver behavior, travel timing, and weather conditions to determine the most efficient travel routes. A weighted graph of Vellore’s road network is constructed, and a heuristic search method is employed to identify the shortest and least-congested routes. Simulation results obtained for the Vellore urban transportation network indicate that the proposed framework reduces average travel time by 17.8% and improves routing accuracy by 12–15% compared with the benchmark routing approaches considered in this study. These simulation-based results show the possible use of the proposed framework for adaptive route guidance, real-time mobility management and decision-support applications in Intelligent Transportation Systems. For operational implementation, more validation in large-scale transportation networks and real-world deployments is necessary. The proposed Multi-Parameter Dynamic Shortest Route Search Algorithm approach is effective in reducing the journey time under dynamic traffic conditions in the simulation scenario considered, resulting in increased fuel economy and the performance of the entire transportation system.</p>