This research investigates predictive maintenance strategies using the AI4I 2020 dataset, a synthetic dataset reflecting real-world industrial environments. The study identifies failure trends, explores their root causes, and evaluates cost impacts associated with different failure types. Advanced analytics reveal heat dissipation failures (HDF) as the most significant contributor to operational disruptions, accounting for over 74% of total maintenance costs. Through a detailed examination of machine operational parameters and failure distribution patterns, this work proposes tailored maintenance solutions, such as enhanced cooling systems, load optimization, and power stabilizers, to mitigate failures effectively. The findings underscore the importance of prioritizing high-impact failures, leveraging predictive maintenance, and adopting proactive strategies to minimize downtime and optimize costs in industrial settings. This research serves as a comprehensive guide for industries aiming to enhance operational reliability and sustainability.