Identifying waste resources in modern-scale cloud infrastructures is a critical sustainability issue since it helps free up additional capacities for extra tasks and improves the performance of the systems while optimizing their costs. It is already well-recognized as a challenging task for human operators regarding manual and massive action efforts. At the same time, the problem is quite complicated-A complete and satisfactory solution is yet to be achieved. The paper proposes a novel and AI-driven approach to the problem. Applying rule induction learning across the history of service deployment instances to the log event data of the underlying entities, we extract conditions that lead to specific patterns, such as Resource Termination, thus providing a predictive mechanism for detecting objects subject to such actions in a real-Time fashion. This explainable recommender system (called Cloud Sweeper) serves as an AI operations assistant for cloud users and Site Reliability Engineers (SRE) in their administrative duties.