Neuromorphic computing represents a groundbreaking shift in the field of artificial intelligence, aiming to replicate the structure and functionality of the human brain in computational systems. Unlike traditional von Neumann architectures that separate memory and processing units, neuromorphic systems integrate these components, enabling faster, energy-efficient, and adaptive learning mechanisms. This emerging technology draws inspiration from neuroscience to create systems that can process information through spiking neural networks (SNNs), synaptic plasticity, and event-driven computation. The convergence of biology and computer engineering within neuromorphic computing offers a transformative potential to bridge the gap between brain-like cognition and machine intelligence. The technology enables real-time sensory processing, adaptive learning, and autonomous decision-making, which are central to the development of nextgeneration intelligent machines. Over the past decade, research has advanced rapidly with hardware prototypes such as IBM’s TrueNorth, Intel’s Loihi, and BrainScaleS, which demonstrate scalable neuromorphic architectures capable of simulating millions of neurons and synapses. The interdisciplinary nature of neuromorphic computing—spanning neuroscience, electrical engineering, computer science, and artificial intelligence—presents both immense opportunities and formidable challenges. Key challenges include the development of efficient learning algorithms compatible with spiking models, hardware scalability, and alignment with cognitive models. Nonetheless, the integration of neuromorphic principles into AI and robotics is paving the way for systems capable of perception, reasoning, and adaptation comparable to biological intelligence. This research explores how neuromorphic computing bridges the gap between biological and artificial cognition, examining its foundations, methodologies, and potential applications across multiple domains.