AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing

The rapid advancement of 5G networks, coupled with the increasing complexity of resource management, traffic handling, and dynamic service demands, has underscored the need for more intelligent network optimization techniques. This paper comprehensively reviews AI-driven methods applied to 5G network optimization, focusing on resource allocation, traffic management, and network slicing. Traditional models face limitations in adapting to the dynamic nature of modern telecommunications, and AI techniques-especially machine learning (ML) and deep reinforcement learning (DRL)-offer scalable, adaptive solutions. These approaches enable real-time optimization by learning from network conditions, predicting traffic patterns, and intelligently managing resources across virtual network slices. AI's integration into 5G networks enhances performance, reduces latency, and ensures efficient bandwidth utilization. It is indispensable for handling the demands of emerging applications such as IoT, autonomous systems, and augmented reality. This paper highlights key AI techniques, their application to 5G challenges, and their potential to drive future innovations in network management, laying the groundwork for autonomous network operations in 6G and beyond.