CMAAF, the Comprehensive Methodology: Accessible Analyze and Forecast, represents a pioneering methodology aimed at revolutionizing the analysis and forecasting of globally traded agricultural commodity prices. Integrating explainable machine learning and econometric techniques, CMAAF offers a novel approach to forecasting monthly price changes in the medium term. Its innovative methodology prioritizes accessibility, comprehensiveness, accuracy, interpretability, and practicality, ensuring that its insights are available to a wide audience regardless of budget, language or any possible constraints. By providing detailed yet straightforward visual explanations of the drivers behind its forecasts, CMAAF empowers stakeholders in the food security domain to make informed decisions and adapt to market dynamics effectively. This application signifies a significant step forward in bridging the gap between advanced forecasting techniques and practical research in global food markets, with the potential to contribute to more resilient and equitable food systems worldwide.