AI-Driven Optimization for Enhancing Performance, Efficiency, and Personalization in Content Delivery Networks

Abstract— Content Delivery Networks (CDNs) have evolved from static infrastructures into dynamic, intelligent platforms that support the demands of modern digital services. Yet, traditional CDNs continue to face challenges related to scalability, efficiency, and personalized content delivery. This study investigates the integration of Artificial Intelligence (AI) and Machine Learning (ML) into CDN architectures to address these limitations. It explores intelligent mechanisms such as predictive caching, adaptive preloading, real-time quality adjustments, and user-specific content recommendations. These AI-driven enhancements demonstrate significant improvements, including a 20–40% increase in cache hit rates and reduced latency through anticipatory content distribution. The research presents a multi-tiered architecture comprising edge, regional, and central layers, where AI tasks are distributed to optimize both performance and resource utilization. Despite their advantages, AI-powered CDNs introduce new challenges, including data privacy concerns, susceptibility to adversarial attacks, and potential algorithmic biases affecting fair content distribution. To mitigate these risks, the study examines strategies like cache segmentation guided by fairness models and highlights the use of federated learning and robust security protocols to protect decentralized data. The role of hybrid cloud-edge environments is also analyzed, showcasing how scalable cloud resources can support AI operations while maintaining low-latency edge responsiveness. By synthesizing insights from academic research and industry practices, this paper outlines a forward-looking framework for building AI-augmented CDNs that prioritize efficiency, equity, and resilience in global content delivery.

Keywords— Artificial Intelligence (AI), Machine Learning (ML), Content Delivery Network (CDN), Intelligent Caching, Predictive Prefetching, Dynamic Content Adaptation, Personalized Content Delivery, Edge Computing, Cloud-CDN Convergence, Performance Optimization, Efficiency Enhancement, Latency Reduction, Bandwidth Optimization, User Experience, Federated Learning, Real-Time Analytics, Scalability, Next-Generation Infrastructure.