Machine Learning Based Optimization Techniques for 5G Networks

5G networks promise faster speeds, lower latency, and improved reliability, but achieving these benefits requires overcoming complex challenges related to resource allocation, latency reduction, and network stability. Traditional methods often fall short in adapting to the dynamic and diverse demands of modern wireless communication. This paper presents machine learning-based frameworks designed to enhance 5G network performance by enabling smarter, data-driven optimization. By leveraging real-time data, these frameworks improve traffic prediction, resource management, and fault detection, allowing the network to adapt more efficiently to changing conditions. Simulation results (10 runs on 3GPP-aligned data) demonstrate 22% throughput gain and 60% latency MSE reduction versus Proportional Fair (PF) and Round Robin (RR) baselines, confirming practical ML value for 5G deployments. These findings highlight the strong potential of integrating machine learning into 5G networks to create more responsive and efficient systems. Ultimately, this work offers practical and scalable solutions that contribute to advancing next-generation wireless communication and enhancing user experience.