Research on Deep Learning for Mobile Edge Computing (MEC) focuses on deploying advanced neural network models at the network edge to enable intelligent, low-latency processing of data generated by mobile and IoT devices. This area addresses challenges such as limited computational and storage resources at edge nodes, dynamic workloads, energy constraints, and real-time inference requirements. Key research directions include designing lightweight and resource-efficient deep learning architectures (e.g., model pruning, quantization, and knowledge distillation), edge–cloud collaborative inference frameworks, and adaptive task offloading strategies for deep learning workloads. Other emerging topics involve real-time video and image analytics, predictive maintenance and anomaly detection in IoT applications, reinforcement learning for edge resource management, and context-aware deep learning for personalized services. Additionally, research on privacy-preserving and secure deep learning, federated deep learning at the edge, and multi-objective optimization for latency, energy, and accuracy represents significant avenues for advancing intelligent, responsive, and efficient mobile edge computing systems.