The complex socioeconomic landscape of conflict zones demands innovative approaches to assess and predict vulnerabilities for crafting and implementing effective policies by the United Nations (UN) institutions. This article presents a groundbreaking Augmented Intelligence-driven Prediction Model developed to forecast multidimensional vulnerability levels (MVLs) across Afghanistan. Leveraging a symbiotic fusion of human expertise and machine capabilities (e.g., artificial intelligence), the model demonstrates a predictive accuracy ranging between 70% and 80%. This research not only contributes to enhancing the UN Early Warning (EW) Mechanisms but also underscores the potential of augmented intelligence in addressing intricate challenges in conflict-ridden regions. This article outlines the use of augmented intelligence methodology applied to a use case to predict MVLs in Afghanistan. It discusses the key findings of the pilot project, and further proposes a holistic platform to enhance policy decisions through augmented intelligence, including an EW mechanism to significantly improve EW processes, thereby supporting decision-makers in formulating effective policies and fostering sustainable development within the UN.