On the basis of the nature of available datasets, ML can be divided into four main categories: I) supervised learning, II) unsupervised learning, III) semi-supervised learning, and IV) reinforcement learning . Supervised learning uses both input data and corresponding labels for training. Unsupervised learning, on the other hand, deals solely with input data without labeled information. As a combination of supervised and unsupervised learning, semisupervised learning combines mostly unlabeled datasets and a few labeled ones. Reinforcement learning algorithms, such as Q-learning, enable learning by interacting with an environment and receiving feedback. These algorithms underpin the growth of AI, improving system performance by exposing to data and experience. Among the four categories, supervised learning is the most widely used and well-established in assessing and protecting drinking water quality because of its strength in prediction with labeled datasets . Unsupervised learning is also useful in safe drinking water supply for tasks such as identifying the major factors affecting water quality and classifying data. Conversely, in safe drinking water supply, the application of semi-supervised learning is scarce, and reinforcement learning is unexplored.