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As depicted in the Practice, PPM procedures play an important role of refining operations in industrial applications in view of the industry 4.0 environment. This research focuses on the demonstration of integrated digital twins across various industries such as pharma, medtech and F&B. In the pharmaceutical industry, areas that can be addressed through the use of digital twins include, virtual inspections, end-to-end process enhancement, and compliance. The medtech industry uses digital twins for designing and testing prototype medical devices and tools used in surgery to prepare accurately for surgery Digital twins are widely used in the food and beverage industry for manufacturing line optimization and analyzing the flow of products. This research establishes means of error prediction of control valves, VSDs, motors/engines, robots/cobots, pipeworks, magnemotion/linear motor, shunks, and CNC machines in different industrial parts. They are enabled to capture real time data from sensors and use complex models of machine learning to diagnose faults or predict their occurrence causing minimal equipment downtime and reduced maintenance time. Reducing the machine downtime by 42.25% and thereby the mean time to repair (MTTR) by 36% this strategy makes a technical solution for organizations concerned with process efficiency and quality assurance, effective and economical. This study makes headways in establishing the possibility of using predictive maintenance to enhance the implementation of Industry 4.0 concepts for enhanced industrial systems.