This thesis investigates the effectiveness of Condition-Based Maintenance (CBM) and Industry 4.0 technologies in a manufacturing environment. CBM is a proactive maintenance strategy that uses real-time data and predictive analytics to optimize equipment performance and reduce downtime. The study focuses on the integration of advanced technologies such as IoT, big data analytics, and digital twins to enhance maintenance practices. Through a detailed case study of RAMAH Motors, the thesis demonstrates significant improvements in operational efficiency, cost savings, and equipment reliability. It concludes with recommendations for further implementation and optimization of CBM in the automotive industry.