Neural Architecture Search (NAS) is an automated machine learning technique that discovers optimal neural network architectures for specific tasks by algorithmically exploring vast design spaces, replacing manual architecture engineering with principled search methods. NAS emerged as a response to the time-consuming and expertise-intensive process of manually designing network topologies, enabling models to design models—architectures that often surpass human-designed counterparts. The field encompasses three core components: search space definition (the set of possible architectures), search strategy (the algorithm to explore this space), and performance estimation (evaluating candidate architectures), with modern approaches dramatically reducing search costs from thousands of GPU hours to mere hours through techniques like weight sharing and differentiable search. A key insight: the quality of the search space often matters more than the sophistication of the search algorithm, as even random search can find strong architectures in well-designed spaces. Understanding the tradeoffs between search efficiency, architecture quality, and hardware constraints is central to practical NAS deployment across domains from computer vision to natural language processing.