LSTM is a type of recurrent neural network (RNN) architecture designed to learn long-term dependencies in sequential data by using gating mechanisms — input, forget, and output gates — that control what information to keep, discard, or pass forward. Before transformers dominated NLP, LSTMs were the state of the art for speech recognition, machine translation, and time-series forecasting. They still appear in production systems where computational budget is limited.