Supply chain management (SCM) is an important part of modern corporate operations, with efficient route optimization playing a significant role in cost reduction and service level improvement. This paper examines the effectiveness of various optimization techniques, with a particular emphasis on Ant Colony Optimization (ACO) and its comparison to other methods such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Simulated Annealing. ACO, which was inspired by ant foraging behavior, has received widespread recognition for its resilient performance in solving complicated combinatorial problems like the Traveling Salesman Problem and the Vehicle Routing Problem (VRP). This study begins with a detailed literature analysis, emphasizing the current status of optimization strategies in SCM, followed by the identification of research gaps. The experimental setup contains a full explanation of the datasets, parameter adjustment, and computational environment. Performance criteria such as solution quality, computation time, and convergence rate are used to assess the success of each method. Our findings show that, while ACO gives near-optimal solutions and is flexible in dynamic contexts, alternative approaches such as GA and PSO provide competitive performance and faster convergence times under certain scenarios. The discussion centers on the practical implications of these findings for SCM, emphasizing the trade-offs between computational efficiency and solution correctness. This research looks into the possibility of hybrid techniques, which combine the capabilities of various algorithms to achieve better outcomes. This study not only broadens our understanding of SCM optimization methodologies, but it also makes practical recommendations for improving industrial route optimization practices.