Conic optimization is a subset of convex optimization where the feasible region is defined by a conic constraint, typically involving a convex cone such as the positive semidefinite cone or the second-order cone. It is widely used in fields like finance, engineering, and machine learning due to its ability to model and solve complex optimization problems efficiently with polynomial-time algorithms.