The U.S. oil and gas sector, a cornerstone of national energy security and economic output, faces mounting regulatory scrutiny and evolving tax compliance mandates at federal, state, and local levels. Amid increasingly complex reporting structures and jurisdictional variations, traditional compliance frameworks—often manual, fragmented, and reactive—struggle to ensure tax accuracy, detect evasive behavior, and optimize revenue recognition. This paper explores the transformative potential of artificial intelligence (AI) in reimagining real-time tax compliance mechanisms across the upstream, midstream, and downstream segments of the petroleum value chain. Beginning with a macro-level overview of compliance challenges—ranging from dynamic severance tax regimes to fluctuating royalty obligations—this study highlights how the industry’s legacy systems are ill-equipped to handle the scale and velocity of modern regulatory demands. AI technologies, including machine learning, robotic process automation (RPA), and natural language processing (NLP), offer a paradigm shift by enabling intelligent data ingestion, anomaly detection, audit trail transparency, and real-time rule enforcement. Through integration with enterprise resource planning (ERP) and volumetric reporting platforms, AI-driven systems automate filings, flag inconsistencies, and align operational data with tax calculations. The study further examines use cases and pilot deployments demonstrating AI's impact on curbing underreporting, reducing audit burdens, and increasing fiscal transparency. The paper concludes with a forward-looking roadmap for public-private collaboration, data standardization, and regulatory innovation to institutionalize intelligent automation across the sector. In doing so, AI not only enhances compliance efficiency but also unlocks significant revenue potential for both industry operators and government agencies.