Efficient route planning is a critical component of modern rapid rail transportation systems, particularly in rapidly growing tier-2 cities where urban expansion places increasing pressure on mobility infrastructure. Computer-based shortest path algorithms play a vital role in optimizing route selection by minimizing travel distance, time, and operational costs. This research paper presents a comparative performance analysis of widely used shortest path algorithms-namely Dijkstra’s Algorithm, Bellman–Ford Algorithm, A* Algorithm, and Floyd–Warshall Algorithm-applied to rapid rail route planning in Meerut City. Algorithmic performance is evaluated based on computational efficiency, accuracy, scalability, memory usage, and suitability for real-time route planning. Simulation results reveal that while Dijkstra’s algorithm performs efficiently for static networks, A* demonstrates superior performance for real-time route queries due to heuristic optimization. Floyd–Warshall, although comprehensive, exhibits high computational cost, making it unsuitable for large-scale urban networks. The study contributes to intelligent transportation system research by linking algorithmic performance with real-world urban rail planning requirements.