Stochastic Programming optimizes decision-making under uncertainty when probability distributions of uncertain parameters are known. Two-stage stochastic programming: first-stage decisions (x) made before uncertainty realization, second-stage decisions recourse actions after observing random parameters ξ. Formulation: min cᵀx + E_ξ[Q(x,ξ)] where Q(x,ξ) = min{qᵀy : Wy ≥ h - Tx}. Solution methods: L-shaped algorithm (Benders decomposition), scenario-based approaches, and sample average approximation (SAA). Multi-stage extensions model sequential decision-making. Applications: capacity planning, financial planning, energy systems, and supply chain management under demand uncertainty.