The AI- Powered Treasury: A Data- Driven Approach to managing America’s Fiscal Future

The growth in the United States national debt has become a major issue leading to concern regarding the long-term fiscal sustainability and economic stability of the economy that will reach over 33 trillion in 2023. The traditional methods of managing debt, which are mostly based on the use of fixed econometric models and manual forecasts, may not be up to the dynamic nature of the modern financial markets, changes in the behavior of the taxpayers, and the real-time changes in the government revenue streams. This study examines how fiscal management can be transformed by the use of artificial intelligence (AI) and data analytics to improve forecasting and Treasury bond auction strategies and minimize the cost of borrowing. In the study, the U.S. Government Revenue Collections dataset (20042023) is employed that offers the data on federal revenues at the daily level, by tax type, payment type, electronic type, and the value of collection. The data contains both numerical and non-numerical features, which allow generalizing the trends and artificial intelligence-based forecasting approaches. Results find a strong trend to digitalization of revenue collection as Internet and wholly electronic transactions are gaining dominance over the old ones, including bank, mail, and over-the-counter transactions. Time-series modeling demonstrates the capability of AI to provide more precise revenues forecasts and simulate the results of policies in changing the debt to GDP ratio to help policymakers predict risks and opportunities in the fiscal environment. Besides, effective debt management with the help of AI is able to cut down the interest expenses, leaving some essential funds to reinvest them in infrastructure, healthcare, defense and other national concerns. The ethical issues, such as transparency in the AI decision-making, and cyber security threats, data privacy, and fair access to the digital systems are also touched upon, and the necessity of responsible use of new technologies is highlighted. This paper concludes that AI-driven fiscal management does not replace human judgment but is an auxiliary tool that makes decision-making easier, more accountable, and fiscally resilient in the long-term. Through blending technological novelty and a sustainable policy formulation, the U.S can be better placed to have a more stable fiscal future in a more data-driven global economy.