Clustering involves dividing the network into clusters, with each cluster featuring a cluster head who responsible for the collection, the aggregation, and the transmission of data to the base station, along with member sensors that is on charge of collecting environmental data and transmitting it to the cluster head. This process aims to extend the network’s lifespan and optimize sensor energy consumption, given the limited capacity of their small, non-rechargeable batteries. Choosing the best cluster head and creating appropriate clusters are the major challenges. Various protocols have been proposed, utilizing the K-means algorithm for cluster formation. This survey paper aims to offer a comprehensive overview of clustering techniques that employ K-means in the cluster formation process. This includes examining how these techniques address the selection of the optimal number of clusters, the initial centroids, and the choice of the cluster head.