The recent developments in the field of environmental monitoring and resource management have seen significant advancements leveraging remote sensing and machine learning techniques. Researchers are increasingly turning to satellite imagery and explainable AI models to address critical issues such as methane leakage from abandoned oil and gas wells and the identification of baseflow in hydrological models. The integration of multi-spectral data with computer vision algorithms is proving effective in pinpointing environmental hazards at scale, while novel neural network architectures are enhancing the accuracy and transparency of hydrological predictions. Additionally, the utilization of spatiotemporal data analytics is revolutionizing outage management systems, providing precise fault location insights crucial for post-event analysis. These innovations collectively underscore a shift towards more data-driven and scalable solutions in environmental and resource management, with a focus on improving efficiency and reducing environmental impact.