This paper, employing a qualitative meta-synthesis approach, explores the role of artificial intelligence (AI) algorithms in predicting, evaluating, and implementing public policies, with a particular focus on strengthening social resilience. The central research question is how AI can be leveraged to im-prove policymaking processes and enhance the capacity of societies to with-stand crises. Findings, drawn from the analysis of 20 scholarly sources, one international report, and five national policy documents from Iran, reveal that AI—through big data analytics, predictive modeling, and automated systems—has the potential to optimize governmental decision-making, reduce human errors, and enable more equitable resource allocation. At the predictive stage, machine learning algorithms support the early identification of emerging risks and trends; in the evaluation phase, data-driven dashboards and real-time analytics enhance policy effectiveness; and during implementation, intelligent systems improve the efficiency and accountability of public services. Nevertheless, challenges such as inadequate data infrastructure, ethical concerns, and algorithmic biases may hinder the realization of these potentials. The case of the COVID-19 pandemic in Iran illustrates that re-sponsible application of AI can promote citizen engagement, improve re-source distribution, and strengthen public trust, thereby fostering social resilience. The study proposes a conceptual framework that emphasizes transparency, ethics, and accountability as prerequisites for integrating AI into policymaking. Policy recommendations include establishing legal frameworks, promoting algorithmic transparency, and investing in data infrastructures to transform AI into a practical tool for resilient governance.
