AI-Driven Smart Caching for Optimized Web Performance and Reduced Latency

The increasing demand for faster and more efficient web browsing has led to significant challenges in caching mechanisms. Traditional caching algorithms, such as Least Recently Used (LRU) and Least Frequently Used (LFU), rely on static rules and do not adapt to dynamic user behavior or real-time network conditions. This research introduces an AI-driven smart caching system that utilizes Machine Learning (ML) and Reinforcement Learning (RL) to predict frequently accessed content and optimize cache allocation dynamically. The proposed model continuously learns from user interaction patterns, network latency and content popularity to enhance cache hit ratios and minimize redundant server requests. By implementing deep learning-based predictive caching, the system reduces web page load time, bandwidth usage and computational overhead on web servers and Content Delivery Networks (CDNs). A comparative analysis with traditional caching methods demonstrates improved performance in terms of response time, storage efficiency and network bandwidth utilization. This study highlights the potential of AI-powered caching strategies in modern web applications, cloud services and mobile browsing environments. The research contributes to the growing field of AI-driven network optimization by presenting a scalable and adaptive caching mechanism. Future developments could include integrating federated learning for privacy-preserving cache predictions and extending the model to support edge computing and 5G-based web infrastructure.