Recurrent Neural Networks (RNNs) for edge intelligence have become a promising research direction as IoT devices and edge nodes increasingly require real-time, low-latency, and resource-efficient AI solutions. Unlike centralized cloud-based processing, edge intelligence leverages local computation to process streaming data closer to its source, reducing bandwidth usage and improving responsiveness. RNNs, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, are widely studied for handling sequential and temporal data generated by sensors, smart devices, and cyber-physical systems. Research explores applications such as predictive maintenance, human activity recognition, speech and video analytics, anomaly detection, and intelligent transportation, where temporal dependencies are crucial. To overcome edge constraints, studies investigate lightweight model compression, pruning, quantization, and knowledge distillation, along with energy-efficient hardware accelerators for RNNs. Recent advancements integrate federated learning, distributed training, and neuromorphic computing to enhance privacy, scalability, and robustness. These efforts establish RNNs as a core enabler of edge intelligence, balancing accuracy, efficiency, and adaptability in real-world IoT and edge environments.