Cloud computing has gained significant popularity in recent years; however, security remains a major concern. Machine learning-based approaches have emerged as a promising solution for addressing security issues in the cloud. This paper presents a comparative analysis of classification algorithms used for detecting and preventing attacks and security gaps in cloud-based applications. The study evaluates various classifiers using performance metrics such as accuracy, precision, recall, and mean absolute error. The analysis is conducted on the dataset, KDD Test. The results highlight significant differences in the performance of classifiers, indicating that some algorithms consistently outperform others. The top-performing classifiers are identified, including Random Forest, Bagging, and J48, which demonstrate high accuracy in detecting and preventing attacks. These findings contribute to the selection and deployment of effective classification algorithms for cloud computing security. The study emphasizes the importance of informed algorithm selection to enhance security measures and mitigate potential threats in the cloud environment.