This paper examines the convergence of triple-entry accounting and machine learning technologies as a transformative approach to enhancing financial transparency and trust in increasingly complex organizational environments. Triple-entry accounting, which adds a cryptographically secured third entry to traditional double-entry bookkeeping, creates an immutable transaction record that addresses fundamental limitations in conventional accounting systems. When integrated with advanced machine learning techniques, this combined approach offers unprecedented capabilities in fraud detection, audit efficiency, and financial verification. Our analysis demonstrates how this technological synthesis can mitigate trust and verification challenges, reduce fraud susceptibility, eliminate reconciliation inefficiencies, simplify regulatory compliance, and provide real-time financial visibility. Through examination of implementation approaches, early adoption case studies, and performance metrics, we identify significant improvements in reconciliation time, fraud detection, audit efficiency, and reporting speed. While technical, organizational, and regulatory challenges remain, the integration of triple-entry accounting with machine learning represents a promising new paradigm for financial systems that balances innovation with the core accounting principles of accuracy and transparency.