Effective management of distributed natural resource systems is a critical challenge in modern engineering due to the dynamic conditions under which storage facilities, environmental reservoirs, and supporting infrastructure operate. These systems are often exposed to gradual degradation processes that can remain undetected until system failure occurs. Traditional monitoring approaches typically rely on periodic inspections or fixed threshold-based sensors, which provide limited capability for predicting emerging risks. Recent advancements in Internet of Things (IoT) sensing technologies and machine learning techniques present new opportunities to transition from reactive monitoring to predictive and intelligent system management. By integrating real-time sensor data with temporal machine learning models, it becomes possible to detect anomalies and forecast hazardous conditions before critical failures arise. This study proposes an intelligent monitoring framework that combines IoT-enabled sensing, edge-level data processing, and cloud-based machine learning analytics to enhance the reliability and responsiveness of distributed resource systems. The framework emphasizes a multi-layer architecture that supports scalable communication, adaptive alert generation, and continuous data-driven learning. The proposed approach addresses the limitations of traditional monitoring systems by enabling proactive maintenance, improved operational efficiency, and more resilient management of distributed natural resources.