Water quality monitoring remains crucial for environmental sustainability, public health, and economic stability. Traditional methods, which are manual sampling and laboratory tests, despite being accurate, may sometimes be resource-intensive, limited in scale, and not frequent enough. In this study, we present a new approach to integrate remote sensing and GIS techniques along with machine learning for near-real-time water quality assessment. This research feeds satellite information collected from the Landsat and Sentinel missions under the Copernicus program into an algorithm that extracts and processes surface water information to derive estimates of critical parameters such as turbidity, chlorophyll concentration, and deposition of pollutants. Amazon Web Services (AWS) Lambda is important in this pipeline by utilizing serverless automated data collection and processing. With the help of spatial analysis and predictive modeling, this research provides a complete picture of the future trends in water quality. By doing so, the findings will aid in policy and management for environmental purposes, and they will be a solution for sustainable management of water resources with an emphasis on low cost and data-driven. This research links the gap between traditional methods and modern technologies, presenting evidence for the viability of cloud-supported, AI-based systems in large-scale environmental monitoring.