Inventory Optimization is the process of determining optimal stock levels, replenishment policies, and allocation strategies across a supply chain to minimize costs while meeting service level requirements. It draws on operations research, statistical modeling, and machine learning to balance trade-offs between holding costs, stockouts, and order frequency under demand uncertainty. Applications span retail, manufacturing, healthcare, and logistics, where AI-driven approaches improve responsiveness and reduce waste across multi-echelon distribution networks.