Long Short-Term Memory (LSTM) is a type of recurrent neural network architecture designed to remember information for long periods, addressing the vanishing gradient problem inherent in traditional RNNs. It uses a gating mechanism to control the flow of information, allowing it to learn dependencies in sequential data effectively.