The recent advancements in remote sensing and deep learning are significantly reshaping the landscape of environmental and urban studies. A notable trend is the integration of high-resolution satellite imagery with sophisticated machine learning models to address complex issues such as urban heat islands, poverty estimation, and land use classification. These technologies are enabling more precise and timely assessments of environmental conditions, offering new insights for policy-making and resource management. Additionally, the incorporation of intra-annual data and 3D city modeling is enhancing the accuracy of analyses, particularly in regions with limited data availability. The field is also witnessing innovative approaches to solar potential analysis and drought prediction, leveraging advanced algorithms to optimize resource utilization and mitigate climate risks. Notably, deep learning models are proving to be efficient alternatives to traditional numerical methods in estimating ground-level air temperatures, offering faster and less computationally intensive solutions. Overall, the synergy between remote sensing and machine learning is driving significant advancements, promising more informed and effective strategies for sustainable development.