Summary: This study explores the integration of IoT and AI for advanced predictive maintenance systems, employing the MOORA method to evaluate various condition monitoring models. By leveraging real-time data exchange from IoT devices and AI-driven analytics, the study demonstrates the shift from reactive to proactive maintenance strategies. Image-based drone surveys and machine learning models, such as decision trees and neural networks, enhance failure prediction accuracy. Model E ranks highest, showcasing its effectiveness in predictive maintenance for industrial equipment.