For decades, inventory management relied on reactive spreadsheets, historical gut feelings, and lagging sales reports, frequently resulting in two costly business extremes: frustrating stockouts that turn customers away, or massive overstocking that ties up essential working capital in warehouse storage. Today, integrating **machine learning in inventory management** transforms procurement from a guessing game into a proactive, data-driven science. By analyzing historical sales trends, local seasonal shifts, real-time website traffic, and economic indicators, machine learning algorithms forecast future product demand with astonishing precision. Limitations of Traditional Inventory Forecasting Traditional inventory planning methods struggle to keep pace with modern supply chain volatility: