Abstract

The potential of Artificial Intelligence (AI) in optimizing network performance and efficiency has garnered significant attention due to its capacity to automate complex processes, enhance decision-making, and improve network resource management. This paper explores the application of AI-driven techniques, including machine learning, reinforcement learning, and deep learning, in the context of network optimization. It examines the evolution of network management practices, from traditional optimization methods to integrating AI technologies, emphasizing their role in addressing key challenges such as congestion, latency, and scalability. The paper reviews current AI methodologies employed in network management, highlights the performance metrics used for optimization, and identifies the challenges inherent in AI adoption, such as data quality, computational power, and integration with legacy systems. Through case studies in telecommunications, cloud networks, SDN, and IoT, the paper demonstrates the impact of AI-driven solutions in enhancing network performance across various sectors. Finally, the paper discusses the limitations of AI in network optimization and provides recommendations for industry stakeholders on effectively integrating AI technologies into existing infrastructures for improved network efficiency and resilience.