The scope of this book spans three chapters, each building systematically upon its predecessors. Chapter 1 establishes the classical foundations of inventory theory, reviewing deterministic and stochastic models that have defined the field for decades. Chapter 2 introduces the fundamental concepts of artificial intelligence and machine learning as they pertain to operations research, providing the technical vocabulary necessary for the chapters that follow. Chapter 3 examines fuzzy logic and soft computing approaches to inventory optimization under uncertainty, demonstrating how imprecise and linguistic information can be formally incorporated into decision models. We trust that this book will serve as a useful contribution to the growing body of literature at the nexus of artificial intelligence and operations management, and that it will inspire further research into the many open problems that remain in this domain.