Water quality monitoring has become increasingly important in recent years, not just for environmental balance but also for supporting life on Earth. Traditional approaches, while successful, may lack the breadth of coverage and timeliness that new concerns necessitate. This study goes into the cutting-edge field of remote sensing, emphasizing its revolutionary role in water quality evaluation. From primitive aerial instruments to sophisticated spaceborne sensors, technology has paved the path for more comprehensive water monitoring capabilities. The spectrum transfer process between water substances and sensors is a significant emphasis, clarifying the complex interactions that form the foundation of these techniques. Recent advances in convolutional neural networks aimed at estimating phytoplankton pigments are particularly noteworthy. Furthermore, the research compares the various inversion models utilized in remote sensing. Advanced applications, such as salinity remote sensing, are examined, emphasizing the remarkable precision they provide. While the gains are promising, the report also shines light on the problems faced, particularly in the widespread adoption of satellite remote sensing, and makes recommendations for the future. This comparative study, which combines two complicated studies into one continuous story, intends to give academics and professionals with a comprehensive understanding of the current capabilities, limitations, and future possibilities of remote sensing in water quality evaluation.