Unexpected equipment failures in production lines pose substantial financial risks, jeopardize worker safety, and diminish overall productivity. Leveraging process mining (PM) for insights from event data, this paper introduces an innovative approach that integrates process mining with the Fuzzy TOPSIS method to enhance predictive maintenance decision-making. The proposed solution is specifically applied to vibration sensor data within an automotive manufacturing line. Fuzzy TOPSIS prioritizes measure based on temperature and axial, horizontal, and vertical vibration criteria. This method enables the early identification of potential fault, thereby mitigating downtime, reducing repair time, and minimizing the adverse impact on production. This integrative approach PM—FTOPSIS (Process Mining—Fuzzy TOPSIS) holds promise for proactive and efficient maintenance strategies in industrial settings.