This article delves into the history and development of data clustering, with a specific focus on the application of the K-means algorithm. Data clustering is a formal study of grouping or clustering objects based on their intrinsic characteristics or similarities. Unlike classification, clustering analysis does not use pre-existing labels to identify objects. This article also discusses several advancements and extensions of the K-means algorithm, as well as its varied applications in different fields.