Train scheduling is an important railway process that helps to establish train arrival and departure times at station to prevent accidents between different trains. This study investigates the train scheduling problem (TSP) with variable train precedences on a single-track Indian Railway (IR) line. To begin, a good model for a TSP has been built using mixed-integer programming, and the Ant Colony Optimisation (ACO) approach has been devised to tackle the problem. Several numerical experiments are carried in which the total travel time is minimized and different strategies are applied to solve track conflicts. Finally, the Indian Railway (IR) manual solution approach is compared to the solution produced using the proposed speed-up strategies and CPLEX solver. The proposed speedup strategies result in a 6.827% reduction in overall trip time when compared to the manual solution method. Based on the order of the trains, a collision-free time is calculated. The novelty of article is (a) the structure for real-time operational practices of IR for the TSP, (b) ACO and CPLEX dependent approach to solving the problems with train schedules in IR and (c) the model validation on the real rail network for Indian Railways and performance evaluation with real data. The outcomes achieved by addressing these issues demonstrate a significant 3.1%, 6.9% improvement in the ACO method’s solution compared to manual approaches. Similarly, in MATLAB, an optimal solution is constructed by identifying conflicts with a conflict detection block.