This research explores the application of the reinforced ant colony algorithm to enhance fault prediction and maintenance strategies for fitness equipment. By adapting this swarm intelligence algorithm, traditionally used in manufacturing and logistics, to the context of sports fitness, the study offers a novel approach to scheduling and predictive maintenance tasks. This method not only addresses complex optimization problems effectively but also improves the adaptability and efficiency of maintenance operations in sports facilities. The algorithm's enhanced capabilities allow for better management of equipment maintenance schedules, ensuring higher availability and reliability of fitness apparatuses, ultimately supporting athlete training regimes by minimizing equipment downtime.