Predictive maintenance has become a critical focus for global manufacturing companies, with advancements in artificial intelligence (AI), big data analytics, and the Industrial Internet of Things (IIoT) already demonstrating significant potential. However, the widespread adoption of these technologies has also revealed several challenges that impede the full digitalization of maintenance operations. This project aims to develop an Integrated Manufacturing Analytics Platform (IMAP) that leverages key Industry 4.0 technologies, including IIoT, digital twins, and advanced analytics, to unlock the full potential of predictive maintenance and move toward prescriptive maintenance. IMAP will integrate and validate data from existing IoT infrastructure with simulated data from lean digital twins, enabling robust preprocessing and integration into the Computerized Maintenance Management System (CMMS). Machine learning, analytics, and optimization techniques will be applied to monitor equipment health, predict maintenance needs, and automate maintenance actions and work orders. Additionally, the platform will incorporate image anomaly detection to enhance its analytical capabilities and support a more comprehensive approach to maintenance operations.