This paper provides a comprehensive review of clustering algorithms, analyzing their underlying principles, data point assignment, dataset capacity, predefined cluster numbers, and application areas. Clustering algorithms are essential in various fields such as marketing, healthcare, and social media, where they help organize and analyze data. The paper discusses the strengths and weaknesses of different clustering methods, including partition-based, hierarchical, density-based, grid-based, and model-based clustering. It also classifies algorithms based on whether they are hard or soft clustering, and by dataset size (small, medium, large). The paper highlights the importance of determining the optimal number of clusters using methods like the elbow method, silhouette score, and gap statistics. It also discusses evaluation metrics for clustering, including internal and external metrics, and their applications in different domains. The paper emphasizes the need for adaptable clustering algorithms to handle diverse data types and complex data structures. It also discusses current trends and future directions in clustering research, including the integration of deep learning techniques. The review concludes that no single clustering algorithm is universally applicable, and the choice of algorithm depends on the specific task and data characteristics. The paper provides a detailed classification of clustering algorithms and their applications, helping researchers and practitioners select the most suitable method for their needs.This paper provides a comprehensive review of clustering algorithms, analyzing their underlying principles, data point assignment, dataset capacity, predefined cluster numbers, and application areas. Clustering algorithms are essential in various fields such as marketing, healthcare, and social media, where they help organize and analyze data. The paper discusses the strengths and weaknesses of different clustering methods, including partition-based, hierarchical, density-based, grid-based, and model-based clustering. It also classifies algorithms based on whether they are hard or soft clustering, and by dataset size (small, medium, large). The paper highlights the importance of determining the optimal number of clusters using methods like the elbow method, silhouette score, and gap statistics. It also discusses evaluation metrics for clustering, including internal and external metrics, and their applications in different domains. The paper emphasizes the need for adaptable clustering algorithms to handle diverse data types and complex data structures. It also discusses current trends and future directions in clustering research, including the integration of deep learning techniques. The review concludes that no single clustering algorithm is universally applicable, and the choice of algorithm depends on the specific task and data characteristics. The paper provides a detailed classification of clustering algorithms and their applications, helping researchers and practitioners select the most suitable method for their needs.