This study presents a Data-Centric Sustainable Financial Governance (SFG) Framework that integrates accounting integrity, audit assurance, tax compliance, and ESG principles. Conventional governance mechanisms often operate independently, resulting in fragmented management of financial information, limited transparency, and inconsistent regulatory application. To address these gaps, the proposed framework leverages Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between Data-Centric Accounting Systems (DAS), Audit Assurance (AA), Tax Compliance Modules (TCM), and ESG integration (ESG) as predictors of SFG. A sample of 220 finance professionals from various industries was analyzed. Empirical results indicate that DAS (β=0.284, p<0.001), AA (β=0.325, p<0.001), TCM (β=0.241, p<0.001), and ESG (β=0.198, p<0.001) positively influence SFG, although the model’s explanatory power was limited (R²=0.874). These findings support the theoretical alignment between digital data integration and sustainable governance while highlighting the need for enhanced construct reliability and contextual flexibility. This research is novel in combining conventional financial functions and ESG-driven data analytics within a single empirically validated model. The hybrid framework identifies performance and data integrity gaps across audit and tax systems, promoting scalability, transparency. The study provides a robust, data-driven foundation for policymakers and financial institutions to advance effective, ethical, and sustainability-oriented financial management practices.