Research on Recurrent Neural Networks (RNNs) for Edge Intelligence focuses on leveraging RNN architectures—such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit)—to enable intelligent processing of sequential and time-series data directly at edge devices, reducing latency and reliance on centralized cloud resources. This area addresses challenges related to limited computational and memory resources, dynamic workloads, energy efficiency, and real-time decision-making at the network edge. Key research directions include designing lightweight and energy-efficient RNN models for edge deployment, adaptive model compression and pruning techniques, and real-time sequence prediction for IoT and sensor networks. Other emerging topics involve collaborative RNN training across edge nodes (federated or distributed learning), anomaly detection in streaming data, predictive maintenance for industrial IoT, and context-aware RNN applications for smart cities and healthcare monitoring. Additionally, research on hybrid RNN architectures integrated with CNNs or attention mechanisms, edge–cloud co-inference, and privacy-preserving RNN inference represents significant avenues for advancing intelligent, efficient, and autonomous edge computing systems.