Clustering techniques assume a major role in mitigating security concerns within cloud computing settings. The objective of this study is to assess and contrast different clustering algorithms in terms of their efficacy in augmenting cloud security. The evaluation encompasses various factors, including the accuracy of algorithms, the time taken for performance, and their efficacy in identifying and addressing security problems. The results indicate that the Make Density Based Clusterer and Simple K Means algorithms exhibit superior accuracy rates in comparison to the other algorithms that were assessed. These algorithms demonstrate a harmonious equilibrium among precision and speed of execution, rendering them excellent selections for augmenting the security of cloud systems. Furthermore, the Farthest First algorithm demonstrates the lowest execution time compared to the other examined algorithms, thus emphasizing its effectiveness in rapidly clustering data. In summary, the empirical findings suggest a notable disparity among the clustering algorithms in terms of their efficacy in identifying and mitigating attacks and vulnerabilities in Cloud-based applications. The observed discrepancies in reliability measurements underscore the divergent efficacy of the algorithms. The Make Density Dependent Cluster and Simple K Means algorithms provide superior reliability, indicating their efficacy in bolstering security inside the cloud environment.