Long Short-Term Memory (LSTM) networks, introduced by Hochreiter and Schmidhuber in 1997, have been extensively studied in research due to their ability to overcome the vanishing gradient problem and effectively capture long-term dependencies in sequential data. Numerous papers demonstrate their success in diverse domains, including natural language processing tasks such as machine translation, text generation, and sentiment analysis, speech recognition systems where LSTMs outperform traditional hidden Markov models, and time-series forecasting applications ranging from financial prediction to traffic flow and energy demand forecasting. Variants such as bidirectional LSTMs, attention-enhanced LSTMs, and convolutional LSTM architectures have been proposed to further improve context modeling, spatial–temporal learning, and sequence-to-sequence tasks. Recent research also explores hybrid models combining LSTMs with convolutional neural networks (CNNs), graph neural networks (GNNs), and reinforcement learning for complex applications like video analysis, medical diagnosis, and autonomous systems, solidifying LSTMs as a foundational deep learning architecture with broad applicability and continuing relevance despite the rise of Transformer-based models.