<p>Efficient route optimization is critical for municipal solid waste collection, as poorly planned routes increase operational costs, environmental impact, and resource consumption. This study compares three path optimization algorithms-Ant Colony Optimization, Dijkstra’s algorithm, and the Nearest Neighbour heuristic-in the context of municipal waste collection networks. The algorithms are evaluated on simulated graphs of varied sizes to assess path cost, computational time, and solution stability. Ant Colony Optimization demonstrates higher adaptability, cost-effectiveness, while Dijkstra provides deterministic optimal pathways with lower variance. The Nearest Neighbour approach, although computationally faster, constantly produces inferior routes. Performance disparities were statistically validated using the Wilcoxon signed-rank test. The findings offer practical guidelines for selecting efficient routing algorithms in urban waste collection systems and establish a foundation for future improvements incorporating real-time data and hybrid models.</p>