This article explores the transformative integration of cloud computing, artificial intelligence, and 5G networks, focusing on predictive maintenance and personalized service delivery. The article examines how 5G infrastructure generates unprecedented volumes of data that can be leveraged for intelligent network management through AI-driven analytics. The article presents a novel framework for integrating federated learning with 5G infrastructure to preserve privacy while maintaining prediction accuracy, evaluates deep learning-based anomaly detection algorithms for fault prediction, and develops a cloud-native architecture for dynamic resource allocation. Key areas explored include theoretical frameworks for AI-driven 5G networks, predictive maintenance methodologies that employ diverse machine learning approaches, privacy-preserving AI techniques that protect sensitive user data, and personalized service delivery systems that adapt to user contexts in real time. The findings demonstrate significant improvements in operational efficiency, network reliability, service personalization, and regulatory compliance while maintaining privacy and security.