This project is designed to tackle the most important and perennial problem of tracking water bodies especially in cities such as Chennai where issues of urban development coupled with climate change impacts pose severe water stress. The existing approaches to monitoring water bodies are not effective, can be extremely expensive, and typically do not offer the timely information required for control and management. This study is here targeted to automate the detection and assessment of lakes and reservoirs using satellite images from Sentinel-1 and Sentinel-2 together with AI model YOLO. Here attention is focused on the method of segmentation for water bodies from the satellite images, uses an advanced YOLO segmentation models for its high speed and accurate results, as works for measuring areas of the segmented water bodies by its pixel values and aspect ratios of the satellite images. This paper presents a novel technique that is reusable and relatively inexpensive for providing continuous water quality monitoring, which is essential in decision-making and future resource allocation. This work can enhance real-time monitoring of environment with the view of helping governments and all other stakeholders in the area of water scarcity and its associated risks.