This paper provides a comprehensive review of clustering techniques and their developments. Clustering is defined as an unsupervised learning method where objects are grouped based on inherent similarities. The paper discusses various clustering methods, including hierarchical, partitional, grid-based, density-based, and model-based approaches. It also covers similarity measures, evaluation criteria, and applications in fields such as image segmentation, object recognition, and data mining. The authors highlight the challenges and limitations of each method, such as the curse of dimensionality and the sensitivity to noise. The paper concludes with a discussion on emerging applications and the selection of appropriate clustering methods for different scenarios.This paper provides a comprehensive review of clustering techniques and their developments. Clustering is defined as an unsupervised learning method where objects are grouped based on inherent similarities. The paper discusses various clustering methods, including hierarchical, partitional, grid-based, density-based, and model-based approaches. It also covers similarity measures, evaluation criteria, and applications in fields such as image segmentation, object recognition, and data mining. The authors highlight the challenges and limitations of each method, such as the curse of dimensionality and the sensitivity to noise. The paper concludes with a discussion on emerging applications and the selection of appropriate clustering methods for different scenarios.