An Integrated AI-Driven Agricultural Financial Intelligence Framework for Farm Revenue Forecasting, Tax Compliance Efficiency, and Subsidy Allocation in the United States

U.S. agricultural finance is shaped by volatile commodity prices, weather shocks, changing production costs, complex tax recordkeeping, and program-specific government support. This paper develops an integrated artificial-intelligence-driven financial intelligence framework that directly addresses three linked objectives: forecasting farm revenue, improving tax-compliance efficiency through pre-filing reconciliation and anomaly triage, and optimizing subsidy allocation among administratively pre-screened eligible farms. The architecture combines farm financial records, production and price information, weather indicators, government-payment data, machine-learning models, explainability, hard policy constraints, and human review. A design-science methodology is paired with a reproducible synthetic stress test of 14,000 farm-year observations representing 2,000 farms from 2019–2025. On the 2025 temporal holdout, XGBoost reduced revenue-forecast mean absolute error to $134,867 from $178,245 for a prior-year benchmark and achieved R²=0.973. For synthetic tax-reconciliation issues with 5.6% prevalence, the XGBoost risk model achieved PR-AUC=0.253 and recall=0.339 under a fixed 7% review capacity, compared with PR-AUC=0.127 and recall=0.268 for static rules. Under a fixed $27.85 million support budget, the constrained need model directed 77.0% of funds to the top quartile of measured need and reduced normalized shortfall among high-need farms by 12.1%. These values are proof-of-concept simulation outputs, not claims of deployed USDA, IRS, or farm performance. The contribution is a validation-ready framework for strengthening agricultural financial planning, voluntary tax compliance, transparent program support, productivity, and income stability.