<![CDATA[Regional food vulnerability in Indonesia is a dynamic and multidimensional challenge that requires timely and accurate monitoring. However, the annual Food Security and Vulnerability Atlas (FSVA) remains limited in its ability to capture rapid intra-annual changes in food security conditions, reducing its effectiveness as an early-warning instrument. This limitation became evident during the 2023 El Niño event, which caused significant production shocks that were not reflected in official vulnerability assessments until the following year. This study proposes a Hybrid Multi-Criteria Decision-Making (HMCDM) framework integrating the Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Ridge Regression to generate a dynamic Regional Food Vulnerability Index (RFVI). The framework was evaluated using a 36-month panel dataset covering 30 sub-districts and nine food security indicators. Expert-derived criteria weights were validated through AHP consistency testing (CR = 0.056), while monthly TOPSIS scores were transformed into supervised learning targets using a novel TOPSIS-as-ML-target architecture. Temporal prediction was performed using Ridge Regression with lag-based feature engineering and expanding-window cross-validation. The proposed model achieved strong predictive performance ((R^2 = 0.870), MAE = 0.043, RMSE = 0.061), outperforming standalone Ridge Regression, ARIMA, and Naïve Forecast baselines. Vulnerability classification accuracy reached 97.3%, while Spearman correlation analysis ((\rho = 0.831), (p < 0.01)) confirmed substantial agreement between expert-defined priorities and data-driven feature importance. The results demonstrate that integrating multicriteria evaluation with temporal machine learning can significantly improve food vulnerability forecasting. The proposed framework provides a robust foundation for data-driven early-warning systems and proactive food security policy planning.]]>
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