Cloud computing has become a key technology for modern data storage and processing due to its scalability, flexibility, and cost efficiency. However, its dynamic nature introduces security challenges such as unauthorized access, network intrusions, resource misuse, and abnormal system behavior. Traditional rule-based systems often fail to detect unknown or evolving threats in real time, reducing system reliability.This research proposes an AI-powered scalable anomaly detection framework for secure data processing in cloud architectures. It uses machine learning techniques such as Isolation Forest, Random Forest, and deep learning models to detect abnormal patterns in cloud data. The framework includes data collection, preprocessing, anomaly detection, and automated alert generation for continuous monitoring.

The system supports real-time processing and scalability while reducing false positives. It improves cloud security through early threat detection and predictive analysis. Experimental results show improved accuracy, faster response, and better reliability compared to traditional methods. Future work includes federated learning, explainable AI, and edge-cloud integration for enhanced performance and privacy.