With the increasing prevalence of Distributed Denial of Service (DDoS) attacks in cloud computing environments, there is a growing need for robust and efficient detection mechanisms. This review paper aims to provide a comprehensive overview of the application of ensemble learning methods in enhancing DDoS attack detection within cloud environments. Ensemble learning, a paradigm that leverages the strengths of multiple models to achieve superior performance, has shown promising results in mitigating the challenges posed by DDoS attacks. The paper begins with an exploration of the significance of DDoS attacks in cloud environments, emphasizing the complexities associated with their detection. A thorough examination of the existing literature reveals the limitations of traditional machine learning approaches in addressing these challenges, paving the way for the introduction of ensemble learning as a viable solution. A detailed discussion on the principles of ensemble learning is presented, accompanied by an overview of prominent ensemble methods such as Random Forest, Gradient Boosting, and Bagging. The strengths and weaknesses of these methods in the context of DDoS detection are critically analyzed, providing insights into their effectiveness.To contextualize the discussion, the paper examines commonly used datasets and features in DDoS detection studies. The application of ensemble learning methods, particularly Random Forest, Gradient Boosting, and Bagging, is then explored in detail through a review of relevant literature. Each method's working principle and its efficacy in detecting DDoS attacks in the cloud are thoroughly assessed.Performance metrics, including accuracy, precision, recall, and F1-score, are discussed as essential criteria for evaluating the effectiveness of ensemble learning in DDoS detection. The challenges faced in implementing ensemble learning for DDoS detection and potential avenues for future research are also identified. In conclusion, this review consolidates the current state of knowledge on ensemble learning methods for DDoS attack detection in cloud environments. By synthesizing existing literature and critically evaluating the strengths and limitations of ensemble approaches, this paper contributes to a better understanding of the role of ensemble learning in fortifying cloud security against DDoS threats.