Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to handle time-series forecasting by capturing long-term dependencies in sequential data. Unlike traditional RNNs, LSTMs use memory cells with gates that regulate information flow, preventing issues like vanishing gradients and improving prediction accuracy. In time-series forecasting, LSTMs analyze historical data patterns to predict future values, making them highly effective for financial markets, weather forecasting, and demand prediction. Their ability to retain relevant past information while discarding unnecessary data allows them to model complex temporal relationships. LSTMs consist of an input gate, forget gate, and output gate, which help manage the cell state. This structure enables them to learn both short-term fluctuations and long-term trends in data. When combined with techniques like attention mechanisms or convolutional layers, LSTMs further enhance forecasting capabilities. Despite their advantages, LSTMs require significant computational power and large datasets. However, they remain one of the most powerful tools for sequential data analysis.