The emergence of cloud computing has transformed the way businesses manage and distribute their digital resources. However, it has also introduced new vulnerabilities and risks, with cyber-attacks exploiting the cloud’s elasticity and scalability features. This paper explores the application of Machine Learning (ML) and Deep Learning (DL) techniques to detect and mitigate Economic Denial of Sustainability (EDoS) attacks, focusing on simulations conducted using CloudSim. Our findings demonstrate the efficacy of these techniques in enhancing cloud security. The simulation results indicate that the proposed system can detect EDoS attacks with an accuracy of 94% and reduce false positives by 30% compared to traditional detection methods. These results underscore the potential of ML and DL models in maintaining cloud stability even during high-impact disaster situations.