Water quality assessment is one of the fundamental approaches to environmental management, which guarantees the steady provision of reliable water sources for both human consumption, water-intensive industries, and the preservation of aquatic environments. Common techniques of water quality analysis and testing might not be flawed, but they have preconditions like high costs and duration of testing. In the case of water quality assessment, the potential and practical use of Quantum AI is in a position to process and analyse more extensive and complex datasets. This includes current information gathered from different sources like the sensors, satellite pictures among others and past data. Thus, it can find patterns and relations that classical AI does not see, which would allow predicting further changes in water quality and detecting contaminations on time. This paper aims to discuss the basic concepts of quantum computing and its potential use in water quality monitoring and prediction. It subsequently provides a comprehensive review of existing AI-based water quality methods, highlighting their strengths and drawbacks.