The emergence of Artificial Intelligence (AI) has substantially impacted different fields of human activity, such as economics and public policy. This paper discusses the changing function of AI in economic policy modeling and decision-making, emphasizing its promise to improve the accuracy, effectiveness, and responsiveness of economic management. Economic policy-making has conventionally depended on mathematical modeling, econometrics, and professional judgment. But these traditional methods tend to grapple with constraints like fixed assumptions, data deficiencies, and retrospective feedback loops. AI, with its ability to handle vast amounts of data, identify patterns, and forecast outcomes, provides a potent alternative or supplement to conventional methods. This study examines how AI technologies—such as machine learning, deep learning, and natural language processing—are being applied in areas like macroeconomic forecasting, fiscal policy evaluation, monetary policy adjustments, and labor market analysis. Real-world applications, including central banks using AI for inflation predictions and tax authorities employing AI for fraud detection, are discussed to illustrate the growing relevance of intelligent systems in public decision-making. The study further examines how decision support systems with AI can be developed to allow policymakers to model the results of different policy options in real time. Using qualitative and descriptive research design, the paper evaluates the possibilities and limitations of AI adoption into economic policy settings. Among the main challenges highlighted are data bias, absence of transparency for algorithmic processes, ethical issues, and the possibility of excessive reliance on technology in high-stakes decision-making environments. The paper underscores the importance of leveraging a balanced perspective under which AI supports human decision-making instead of substituting for it. By assessing the evolutionary potential of AI in economic systems, this research adds to the general debate on digital governance and smart policymaking. The results highlight the need for strong regulatory guidelines, cross-disciplinary cooperation, and ongoing assessment processes in order to make sure that AI remains a means of inclusive, responsible, and evidence-based economic policy.