Predictive maintenance for effective resource management in Industrial IoT (IIoT) is a significant research area focused on minimizing downtime, reducing operational costs, and improving overall equipment efficiency through data-driven insights. Research papers in this domain explore the use of IoT sensors, real-time monitoring systems, and machine learning/deep learning techniques to predict equipment failures, schedule maintenance proactively, and optimize resource utilization. Key contributions include AI-driven fault detection, condition-based monitoring, edge/fog-assisted analytics for low-latency decision-making, and integration with digital twins and cloud platforms for centralized management. Recent studies also address challenges such as heterogeneous device integration, energy efficiency, scalability, and secure data communication in industrial environments. By leveraging predictive analytics and IoT-enabled intelligence, research in predictive maintenance aims to enhance operational efficiency, prolong equipment lifespan, and support sustainable and smart industrial ecosystems.