Mobile Edge Computing (MEC) is pivotal in enabling low-latency, high-efficiency services for fifth-generation and emerging sixth-generation networks by processing data closer to end-users. However, dynamic resource management in MEC remains challenging due to fluctuating workloads, energy constraints, and varying user demands. This article presents an AI-driven framework for efficient resource management in MEC, leveraging deep reinforcement learning (DRL) to optimize server provisioning, service replica placement, autoscaling, and request routing. Using real-world traffic data from the Shanghai dataset, we show that DRL-based resource management can reduce access latency and server usage compared to traditional methods such as Voila and random selection. Our proposed architecture improves scalability, energy efficiency, and overall resource utilization, supporting diverse IoT and latency-sensitive applications. Finally, we outline key challenges and future research directions, including mobility management and integration with renewable energy in edge computing ecosystems.
