This paper analyzes the potential of artificial intelligence (AI)-based predictive analytics to enhance efficiency, equity, and accountability in public-sector resource distribution in the United States. Rapid integration of AI technology in government facilities has enabled more data-driven, proactive decision-making, yet concerns remain about efficiency, equity, and accountability. This study adopted a quantitative research design based on secondary data of three official government sources in the United States: the 2024 Federal AI Use Case Inventory provided by the Office of Management and Budget (OMB), USAspending.gov, and the Government Accountability Office (GAO) Report GAO-25-107653. The data was analyzed using descriptive and analytical techniques. The results show that the use of AI-based predictive analytics increases efficiency by streamlining processes, automating administrative tasks, and enabling quicker decision-making. The research also finds that AI can facilitate the equitable distribution of resources by enabling interventions that attend to specific needs. Accountability-wise, despite several agencies having put governance and oversight systems in place, gaps in transparency and independent assessment remain a concern. The research concludes that AI-driven predictive analytics could significantly enhance resource allocation by the public sector in the United States, particularly in terms of efficiency. The study identifies that AI can improve efficiency through automated budget forecasting, equity through algorithmic targeting of underserved communities, and accountability through real-time compliance monitoring systems. The study recommends uniform AI governance policies, institutional capacity building, and increased transparency in AI applications among the government agencies.