Long short-term memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to effectively capture and learn long-range dependencies in sequential data by using a gating mechanism to control the flow of information. It overcomes the vanishing gradient problem that traditional RNNs face, making it suitable for tasks such as speech recognition, language modeling, and time series prediction.