The water Distribution System (WDS) is one of the critical components of the water supply system. Due to its huge sprawl and the large number of components are involved in the systems and thus it is difficult for operation and maintenance when considering it as a whole for providing good quality and quantity of water to the consumers. Due to uncertainty and complex nature and interrelationships of the parameters, makes the system difficult to divide the system into subsystems for the design, control and maintenance of the systems at different stages. It helps to divide the WDS into subsystems to reduce its complexity and improving management, minimizing water losses, reducing infrastructure cost, locate crucial locations for pressure and water quality sensor and improving the overall efficiency of WDS. The main objective of the study is to focus on the different methodologies available for the clustering techniques, such as spatial, topological and statistical clustering etc. The objective of this study is to considered the hypothetical example networks for application of K-means clustering for different purpose for easy operation and maintenance in WDSs. EPANET Example network 3 is considered for dividing the network in three defining different pressure zones using k-mean clustering which will be helpful in identifying leakage zones. Secondly, another example is considered for simplifying the network operation through connectivity analysis. Thirdly, water fraction matrix is used for minimizing the locations of water quality sampling points in water distribution systems. Significance of clustering in water distribution systems are discussed.