The integration of Artificial Intelligence (AI) into tax technology is transforming compliance and efficiency in the U.S. tax system. AI-driven business analytics offers a data-driven approach to enhancing tax administration, reducing compliance burdens, and improving fraud detection. This paper presents a business analytics framework that leverages AI technologies such as machine learning, natural language processing (NLP), robotic process automation (RPA), and blockchain to optimize tax compliance and operational efficiency. Machine learning enhances risk assessment by detecting anomalies and predicting tax fraud patterns, enabling proactive audits. NLP-powered AI systems facilitate real-time interpretation of tax regulations and automate taxpayer assistance, improving service delivery. RPA streamlines tax reporting processes, reducing manual errors and processing times, while blockchain enhances the security and transparency of tax transactions. Additionally, AI-driven tax policy simulations support data-driven decision-making for tax reforms and revenue optimization. Despite its potential, AI-driven tax technology faces challenges, including ethical concerns, data privacy risks, and integration complexities with legacy tax systems. Ensuring transparency, accountability, and fairness in AI-based tax enforcement is critical. Regulatory bodies must establish governance frameworks to oversee AI applications while promoting responsible AI adoption in tax administration. This review highlights key policy recommendations, including AI governance structures, public-private collaborations, and investment in AI literacy for tax professionals. By balancing automation with human oversight, AI-driven tax technology can enhance compliance accuracy, reduce costs, and improve taxpayer engagement. As AI continues to evolve, its role in tax compliance and efficiency will be central to shaping the future of digital tax administration in the United States.