This chapter provides an in-depth analysis of Recurrent Neural Networks (RNNs), focusing on their exceptional ability to effectively process sequential data. We have traced the development of RNNs from their initial architecture to more sophisticated variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). These networks have played a crucial role in tasks that require consideration of context and sequence. Additionally, we discussed the importance of RNNs in the emerging field of Neuro-Symbolic Artificial Intelligence, which paves the way for further investigation into the fusion of neural-based learning and symbolic-based reasoning.