Feature engineering is the process of transforming raw data into meaningful features that improve the performance of AI models. AI assists in automating feature extraction, selection, and transformation through algorithms that identify the most relevant variables for predictive tasks. Techniques like Principal Component Analysis (PCA) and Autoencoders help reduce dimensionality, eliminating redundant or less informative features. AI-driven feature selection methods, such as recursive feature elimination and embedded methods within machine learning models, optimize model accuracy while reducing computational complexity. Moreover, AI can generate new features through deep learning techniques, uncovering hidden relationships within the data that may not be apparent through traditional statistical methods.