The main goal of this research is to develop a comprehensive workflow for using remote sensing data to study the aftermath of a dam collapse. As a case study, the research looks at changes in the Kakhovka reservoir area caused by the dam collapse on June 6, 2023. To evaluate the impact of this disaster, it is essential to analyze key morphometric parameters before and after the failure, the total loss of water surface, changes in soil moisture, and to qualitatively assess the area that has become exposed after water withdrawal; additionally, monitoring land cover changes over the two years following the collapse is vital. The quickest way to do this is by using remote sensing data. The study used Sentinel-2 images from 2020 to 2025. These datasets made it possible to assess changes before and after the dam failure. Image classification before and after the event served as the primary change detection method. Several classification techniques—including random forest, support vector machine, and naïve Bayes—were tested for this purpose. The results showed that support vector machines provided the most effective classification approach for this area. Remote sensing data enabled the identification of geometric and physical changes in the study region. The findings revealed significant changes in the coverage of the Kakhovka reservoir since 2023. The total size of the reservoir was estimated to have decreased substantially. NDVI analysis showed the distribution patterns, and similarities between NDVI profiles were calculated. The areas cleared of water have become vegetated by various tree species and shrubs, indicating a significant shift in the surrounding ecosystem.