Spatial and spatiotemporal clustering algorithms are essential in various fields, such as computer science, artificial intelligence, and geography. These algorithms allow for the identification of clusters that share similarities in both spatial and temporal dimensions, offering insights into patterns and relationships that may not be evident through individual spatial or temporal analyses alone. Identifying various algorithms and their applications based on different data is crustal. Therefore, this study aims to provide a concise overview of the latest research on special and spatiotemporal data clustering algorithms, commonly used in the field of spatial and spatiotemporal data analysis. We conducted the study by analyzing the most recent articles. We employed a methodology that involved a detailed study of each selected article, encompassing its method, implementation, and outcome, followed by the proposal of various ideas for improvement specific to each work.