This research focuses on developing and applying Predictive Maintenance (PdM) technology in the industrial garment industry. The primary objective is to optimize maintenance processes, reduce production interruptions, enhance productivity, and extend the lifespan of equipment. By addressing critical challenges such as high maintenance costs, rapid equipment wear, production delays, and the inability to predict machine failures, this study aims to improve business competitiveness and product quality. The research methodology includes constructing a predictive maintenance model, integrating advanced algorithms to enhance model efficiency, and implementing these solutions within industrial garment operations. The adoption of PdM technology allows businesses to proactively plan maintenance activities, reduce machine downtime, optimize repair costs, and increase equipment reliability. These advancements contribute to improved production efficiency and the ability to meet evolving customer demands. The findings of this study are expected to provide practical benefits for enterprises in the garment industry, enabling them to tackle current challenges and align with modern production trends. This research offers a valuable framework for enhancing operational performance and fostering sustainable growth in the garment industry.