This systematic review explores the integration of demand forecasting and risk mitigation models within responsive supply chains, particularly in dynamic and volatile markets. As global supply chains face increasing uncertainty due to fluctuating consumer demand, geopolitical events, and environmental factors, effective forecasting and proactive risk management have become critical to maintaining operational efficiency. The review synthesizes key findings from existing research on forecasting tools, demand-driven planning, and risk mitigation strategies, highlighting their contributions to creating more resilient and adaptive supply chains. Demand forecasting tools, such as time-series analysis, machine learning algorithms, and artificial intelligence (AI), have proven essential in predicting future demand with greater accuracy. These tools enable supply chain managers to better anticipate consumer behavior, reducing the likelihood of stockouts and overstocking. However, while forecasting methods have advanced, challenges remain in managing volatility and unforeseen disruptions. Risk mitigation models, particularly those focused on supply chain resilience, are crucial for addressing uncertainty. Strategies such as diversification of suppliers, inventory buffers, and real-time monitoring are integral to minimizing the impact of disruptions. The review also examines how supply chain risk management frameworks, including scenario planning and risk assessments, complement demand forecasting to improve overall supply chain responsiveness. The review concludes that a holistic approach merging advanced forecasting techniques with proactive risk management is vital for responsive supply chains. Further research is needed to explore the integration of these models in real-world settings, evaluate their effectiveness in diverse industries, and develop frameworks for continuous improvement in dynamic market conditions.