The integration of Machine Learning (ML) and blockchain technology into accounting oversight systems represents a transformative shift in addressing the limitations of traditional financial governance models, which rely on static ledgers, manual reconciliation, and retrospective audits. This study evaluates the synergistic potential of predictive risk analytics (PRA), dynamic internal control mechanisms (DICM), and decentralized ledger technologies to enhance fraud detection, financial reconciliation, and regulatory compliance in high-risk economic sectors. Drawing on empirical data from public-sector financial records and private-sector supply chains, we demonstrate that a hybrid ML-blockchain system achieves a 9.8% improvement in fraud detection accuracy (F1-score) and a 60% reduction in reconciliation time, while maintaining 99.8% transaction accuracy. The findings validate theoretical frameworks such as triple-entry accounting (Grigg, 2024) and X-Accounting (Faccia et al., 2020), but also reveal critical challenges, including scalability limitations, data privacy trade-offs, and the need for cross-jurisdictional regulatory standards. Stakeholder validation confirms the system’s operational feasibility (95% approval) and regulatory compliance (100% alignment with GAAP/IFRS), though ethical governance (85% approval) and model transparency (90% approval) require further refinement. This study contributes a conceptual architecture for next-generation accounting automation, bridging the gap between traditional compliance models and the demands of modern financial infrastructure, where real-time validation, automation, and transparency are essential.