When it comes to long-range shooting in basketball, the trajectory tracking approach is essential. However, when it comes to tackling this issue, typical particle swarm optimization has its limits and produces less than optimal results. To that end, this study presents an ant colony algorithm-based approach to monitoring basketball players’ long-distance shooting trajectories and analyses that approach thoroughly. As a first step in reducing interference elements in the process of shooting trajectory tracking, the influencing variables were properly detected using the group foraging theory. The indicators were then fairly separated according to the demands of the approach. Next, the shot trajectory tracking technique was built using the ant colony algorithm, and the results were thoroughly evaluated. According to the MATLAB simulation results, there are specific evaluation criteria that demonstrate the shooting trajectory monitoring technique based on the ant colony algorithm outperforms the traditional particle swarm algorithm in terms of accuracy and processing time for the influencing factors.