Important information needed to comprehend the biological processes that happen in a specific organism, and for sure with a relevance to its environment. Gene expression data is responsible to hide that. We can improve our understanding of functional genomics, and this is possible if we understood the underlying trends in gene expression data. The difficulty of understanding and interpreting the resulting deluge of data is exacerbated by the complexity of biological networks. These issues need to be resolved, so clustering algorithms is used as a start for that. Also, they are needed in many files like the data mining. They can find the natural structures. They are able to extract the most effective patterns. It has been demonstrated that clustering gene expression data is effective for discovering the gene expression data's natural structure, comprehending cellular processes, gene functions, and cell subtypes, mining usable information from comprehending gene regulation, and noisy data. This review examines the various clustering algorithms that could be applied to the gene expression data, this is aiming to identify the signature genes of biological diseases, which is one the most significant applications of clustering techniques.