Deep learning for mobile edge computing (MEC) is a rapidly evolving research area that focuses on bringing AI capabilities closer to mobile users and IoT devices to enable low-latency, intelligent, and context-aware services. Research papers in this domain explore the deployment of deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models at edge nodes, addressing challenges such as limited computational resources, memory, and energy constraints of mobile edge devices. Studies emphasize model compression, pruning, quantization, knowledge distillation, and hardware-aware optimization techniques to make deep learning feasible on MEC nodes. Recent works also investigate distributed and collaborative learning paradigms, including federated learning, edge–cloud co-inference, and model partitioning, to enhance scalability, privacy, and performance. Applications span real-time video analytics, autonomous vehicles, smart healthcare, industrial IoT, augmented/virtual reality, and intelligent transportation systems. Additionally, security- and privacy-aware deep learning frameworks are being integrated to protect sensitive data while maintaining high model accuracy and low latency. Overall, deep learning in mobile edge computing enables intelligent, adaptive, and resource-efficient services, bridging the gap between data generation and actionable insights at the network edge.