Digital twins (DTs) are shifting the theoretical idea of PdM and asset management to a more practical concept of physical assets and mirror control of their real-world counterparts. This present research examines how DTs increase the value of PdM by combining Internet of Things (IoT) sensor data with sophisticated machine learning algorithms such as Random Forest Classifiers (RFC), Long Short-Term Memory (LSTM) networks, and autoencoders. Through the use of monitoring, DTs offer a solution to the weaknesses of conventional maintenance approaches whereby, early indications of a system failure are hard to decipher. The outcomes reveal fairly attractive enhancements in inventory, including within the realms of optimum maintenance cost, minimum allowable downtime, and prolongation of equipment life. Component utilization rates rose by 20%, and unplanned downtime reduced by 25%, thus proving the effectiveness of the DT-powered technology economically and from an operational perspective. The limitation in achieving interoperability and dynamic scalability is admitted, implying that greater effort is required and improved technology. Toward this end, this paper lays the groundwork for the use of DTs to drive Industry 4.0 by arguing that they can enhance the operational reliability, productivity, and profitability of industrial operations.