The adoption of the Industrial Internet of Things (IIoT) has enabled real-time monitoring of machinery, leading to increased efficiency and reduced downtime. This paper presents an AI-driven predictive maintenance system that utilizes machine learning algorithms to detect potential failures before they occur. Using historical sensor data, the proposed model achieves high accuracy in predicting equipment malfunctions, allowing timely intervention. The study demonstrates the potential of AI in optimizing industrial processes and reducing operational costs.