Stochastic optimization is a mathematical method used to find optimal solutions in problems that involve uncertainty, randomness, or incomplete information. It leverages probabilistic techniques to efficiently explore the solution space, making it particularly useful in fields like machine learning, finance, and operations research where exact solutions are often impractical or impossible to determine.