Water distribution systems are critical infrastructure networks that supply clean and safe drinking water to urban and rural populations. Managing and optimizing these systems is paramount for ensuring water quality, resource conservation, and system efficiency. It begins with an overview of the challenges and complexities of water distribution systems, emphasizing the need for advanced data analysis techniques. Clustering is a data-driven tool that impacts water distribution system optimization. Subsequently, the paper delves into various clustering algorithms and their adaptations for handling water distribution data. Commonly used clustering methods such as K-means, fuzzy, hierarchical, and D.B.S.C.A.N. are discussed, alongside their strengths and limitations in this domain. This paper offers a comprehensive examination of clustering techniques in the context of water distribution system management; for scholars, engineers, and legislators looking to boost the effectiveness and resilience of these vital infrastructure networks, this study provides an extensive analysis of clustering approaches in the context of managing water distribution systems.