The escalating complexity of modern distributed systems—characterized by microservices architectures, cloud-native deployments, and dynamic resource scaling—has rendered manual performance tuning nearly obsolete. Traditional methods, which rely heavily on human intuition and static rule-based configurations, fail to account for the non-linear interactions between distributed components. This review article explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) as a paradigm shift in system optimization. By leveraging techniques such as Reinforcement Learning (RL), Bayesian Optimization, and Deep Learning, researchers are developing autonomous \"self-tuning\" systems capable of managing memory allocation, query execution, and network latency in real-time. We examine the transition from black-box modeling to transparent, interpretable AI frameworks. This review synthesizes current methodologies, highlights the challenges of training overhead and data drift, and outlines the future trajectory of AI-based tuning, emphasizing the move toward proactive, workload-aware orchestration that ensures high availability and cost-efficiency in large-scale environments.