By employing artificial intelligence (AI) and the Internet of Things, predictive maintenance is revolutionizing financial asset management. The proactive maintenance interventions are enabled by the integration of real-time data captured from IoT devices with sophisticated AI algorithms for predictive analytics. Enabling enhanced decision-making, minimizing maintenance costs, and maximizing asset reliability are the primary objectives of the proposed method. Asset performance has been improved, unanticipated malfunctions have been minimized, and profitability has been increased through the integration of this state-of-the-art predictive maintenance method into financial asset management systems. The process that produced these results is detailed in the paper, which encompasses the predictive maintenance decision framework, data aggregation methodologies, and integration techniques. The results illustrate the potential transformative effects of integrating AI with the IoT for predictive maintenance, which could offer financial institutions a competitive edge by facilitating streamlined operations and improved asset management. The model obtains an accuracy of 95%.