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Distributed Denial of Service (DDoS) attacks remain a major challenge for distributed systems, given the traditional detection mechanisms often fail to address the scalability and complexity of modern network architecture. This paper explores the integration of Artificial Intelligence (AI) techniques for anomaly detection and the proposed approach leverages machine learning models to detect and respond to malicious traffic patterns in real-time. This study presents a comprehensive review of AI based techniques, their application within distributed systems, and comparison against conventional methods. This study also demonstrates that AI driven anomaly detection offers effective defense against evolving DDoS threats.