A recurrent neural network processes sequences one element at a time while keeping a hidden memory of what came before, like reading a sentence word by word and remembering the context. RNNs and their improved variant LSTM long dominated speech recognition, translation, and time-series forecasting. They struggle with very long sequences and slow training, which is why transformers largely replaced them. RNNs remain useful for lightweight tasks.