Active inference is a theoretical framework, rooted in Bayesian principles, positing that biological agents and artificial systems endeavor to minimize prediction error by continuously updating their generative models and selecting actions. This approach investigates how organisms maintain preferred states and interact adaptively with their environment through the joint optimization of perception and action, providing a unified perspective on brain function and behavior.