Spiking Neural Networks (SNNs) are a class of biologically inspired neural networks that model neurons as discrete events or “spikes,” enabling efficient temporal information processing and low-power computation. SNNs differ from traditional artificial neural networks (ANNs) by incorporating spike timing, neuronal dynamics, and event-driven communication, making them particularly suitable for neuromorphic hardware and real-time processing. Early research focused on modeling spiking neuron dynamics, learning rules such as Spike-Timing-Dependent Plasticity (STDP), and simple pattern recognition tasks. Recent advances leverage deep SNN architectures, surrogate gradient learning, conversion methods from ANNs to SNNs, and hybrid models combining spiking and conventional neurons to improve performance on image classification, speech recognition, event-based vision, and robotics tasks. Applications span neuromorphic computing, edge AI, energy-efficient AI systems, real-time sensory processing, and brain-inspired computing. Current research also explores training efficiency, robustness, scalability, and integration with reinforcement learning, establishing SNNs as a promising framework for low-power, biologically plausible, and temporally aware artificial intelligence systems.