This paper introduces the design and implementation of an innovative simulation system for the Ant Colony Optimization (ACO) algorithm, aimed at improving the understanding of the algorithm before applying it to real-world applications, such as drone path planning. Initially, we adopt an object-oriented approach to construct the system framework, facilitating the modular development of various simulation components. We then propose an implementation method for the core tasks of the ACO algorithm, including shortest path discovery and pheromone update mechanisms. Next, we design the system’s GUI for vivid visualization and interactive user engagement, along with several utility tools to aid in debugging and optimizing the algorithm. The simulation results validate the feasibility of the proposed methods for simulating the ACO algorithm by providing an intuitive and comprehensive presentation of several simulation scenarios. Additionally, the results quantitatively verify the algorithm’s convergence speed under various parameter settings.