Reservoir Computing (RC) is a neural network paradigm designed for efficiently processing temporal and sequential data by leveraging a fixed, high-dimensional dynamical system called the “reservoir,” combined with a trainable readout layer. Unlike traditional recurrent neural networks (RNNs), RC does not require training the internal weights of the reservoir, significantly reducing computational complexity while maintaining the ability to model nonlinear temporal dependencies. Early RC models include Echo State Networks (ESNs) and Liquid State Machines (LSMs), which have been applied to time series prediction, speech recognition, and chaotic system modeling. Recent research explores integrating deep architectures, spiking neural networks, and hybrid models to enhance memory capacity, nonlinear representation, and robustness. Applications span robotics, control systems, signal processing, financial forecasting, and neuromorphic computing, where real-time, energy-efficient temporal processing is critical. Current studies also investigate hardware implementations, reservoir optimization, task-specific adaptation, and combination with other machine learning paradigms such as deep learning and reinforcement learning, establishing reservoir computing as a powerful framework for efficient temporal and sequential data processing.