Neural Architecture Search (NAS) is an innovative approach in machine learning that automates the design of neural network architectures. By leveraging algorithms, NAS explores various configurations to identify optimal architectures tailored for specific tasks. This process involves defining a search space, which encompasses potential architectures, and employing performance estimation strategies to evaluate their effectiveness. NAS can utilize different search strategies, including random search, grid search, and more advanced methods like reinforcement learning. As a result, NAS significantly enhances the efficiency and performance of neural networks, making it a vital area of research in artificial intelligence and automated machine learning.