Agricultural policy development faces increasing complexity due to interconnected environmental, economic, and social sustainability goals that must be balanced across diverse farming systems and regional contexts. Traditional policy assessment methods often rely on retrospective analysis or simplified projections, limiting their capacity to anticipate unintended consequences or optimize policy design before implementation. Digital twins—virtual replicas of physical agricultural systems that integrate real-time data, system dynamics models, and agent-based simulations—offer a transformative approach to policy evaluation by enabling dynamic scenario testing and impact forecasting. This article presents a comprehensive system modeling and decision-support framework for deploying digital twins in agricultural policy simulation, addressing data integration from remote sensing platforms, farm-level monitoring systems, and climate models. Core modeling approaches including system dynamics, agent-based modeling, and multi-criteria optimization are examined for their capacity to represent farming system behaviors and sustainability indicators. Applications focus on evaluating subsidy mechanisms, regulatory interventions, and incentive-based policies across dimensions of crop yield, greenhouse gas emissions, water use efficiency, and farm economic viability. The framework incorporates stakeholder-oriented decision-support interfaces that facilitate collaborative policy design and adaptive governance. Challenges related to data uncertainty, computational scalability, and institutional adoption are critically analyzed alongside future research directions integrating artificial intelligence and real-time monitoring capabilities for enhanced policy responsiveness and sustainability outcomes.