<b>Surrogate Modeling of Regulatory Economic Impact: An ANN, SVR, and Random Forest Comparative Study</b> <b><i>"Code & Courts: Machine Learning for Economic Compliance"</i></b>

The intersection of legal frameworks and economic development is central to India's Viksit Bharat 2047 trajectory. Traditional econometric models often fail to capture the complex, non-linear relationships between statutory regulations and real-world economic efficiency. This paper introduces an interdisciplinary approach applying machine learning to legal-economic analysis. We present a generalized surrogate modeling framework to assess the economic impact of regulatory compliance. By comparing Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest models, this research quantifies the drag (compliance costs) and lift (economic growth) generated by specific legal statutes. Utilizing domestic economic datasets juxtaposed against indices of regulatory stringency, the models predict economic outcomes of legislative changes. Findings reveal that while SVR provides robust boundary margins for linear environments, Random Forest and ANN superiorly map the multi-variable impacts of modern commercial laws. This study bridges qualitative legal theory and quantitative economic forecasting, offering policymakers a predictive tool to draft legislation maximizing economic momentum while maintaining legal equity.