The rapid expansion of data generated by digital platforms, sensor networks, and intelligent systems has intensified the demand for models capable of effectively capturing temporal dependencies and sequential patterns. Sequence modeling has therefore emerged as a cornerstone of modern deep learning, enabling machines to interpret ordered data streams such as text, speech, financial signals, and physiological measurements. Unlike traditional feedforward neural networks, recurrent architectures are specifically designed to preserve contextual information across time steps, making them particularly suitable for applications where historical states influence future predictions. Among the most influential approaches in this domain are Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), each introducing architectural innovations to address limitations associated with gradient instability and long-term dependency learning. This review provides a structured and critical examination of these three foundational architectures, highlighting their internal mechanisms, computational characteristics, and suitability for diverse real-world scenarios. Applications spanning natural language processing, speech recognition, healthcare analytics, financial forecasting, and cybersecurity monitoring are synthesized to illustrate the practical significance of sequence-aware learning frameworks. A comparative analysis reveals that while conventional RNNs offer conceptual simplicity and lower parameter overhead, LSTM and GRU models demonstrate superior robustness in handling extended temporal relationships with improved convergence behavior. Furthermore, the study identifies persistent challenges related to model scalability, interpretability, and computational efficiency, particularly in resource-constrained environments. Future research directions are therefore discussed with emphasis on hybrid architectures, lightweight sequence models for edge deployment, and the integration of attention-based mechanisms to enhance adaptability and transparency in next-generation intelligent systems.

Sequence Modeling, Recurrent Neural Networks, LSTM, GRU, Deep Learning, Time-Series Analysis, Natural Language Processing