Accurate demand forecasting is a critical component of supply chain optimization, inventory management, and strategic planning in modern enterprises. Conventional statistical forecasting approaches often struggle to represent nonlinear patterns and abrupt changes in consumer behavior, which reduces their effectiveness in volatile market conditions. This study explores data-driven forecasting techniques that integrate historical sales records with factors influencing purchasing behavior, including seasonality and promotional effects. A comparative experimental analysis is conducted between classical time-series approaches and advanced data-oriented predictive models. The results demonstrate that data-driven forecasting techniques achieve higher predictive accuracy and stability, particularly when long-term temporal dependencies and irregular demand fluctuations are present. The proposed approach supports improved decision-making by reducing forecasting errors and enhancing operational efficiency. The findings highlight the potential of intelligent forecasting systems for sustainable business growth and adaptive demand planning.