Recurrent Neural Networks (RNNs) are a specialized class of neural networks designed to process sequential data. Unlike traditional feedforward networks, RNNs utilize internal memory to maintain contextual information across time steps, making them ideal for tasks such as language modeling, time series forecasting, and speech recognition. This chapter delves into the architecture and functioning of RNNs, discusses key variants like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), and highlights their applications across various domains. We also explore challenges such as vanishing gradients and computational inefficiencies, along with contemporary solutions and future directions for RNN research.