This study leverages machine learning (ML) and explainable artificial intelligence (XAI) to predict complex phenomena, specifically focusing on forecasting food security and nutritional status in Madagascar up to 2030. By combining Support Vector Machines (SVM) with SHapley Additive exPlanations (SHAP), we aim to provide both accurate predictions and transparent, interpretable insights into the decision-making process . The predictive targets include food insecurity, underweight, chronic malnutrition, and acute malnutrition, addressing critical issues related to public health and nutrition. The robustness of SVM in handling high-dimensional data, coupled with SHAP's capability to explain individual predictions, ensures that our model not only delivers reliable forecasts but also offers clarity on the importance of each feature. The methodology involves collecting diverse datasets from reputable sources, performing exploratory data analysis, and implementing SVM for predictive modeling. SHAP is then utilized to enhance model interpretability by providing detailed explanations of feature contributions. Our contributions include methodological advancements in integrating XAI with ML, development of transparent predictive models for decision-makers, and practical applications to real-world challenges in Madagascar. This research aims to support the Sustainable Development Goals by offering actionable insights for improving food security and nutrition, while also advancing global understanding of complex predictive phenomena.