Affordable housing programs in the United States face persistent challenges related to financial mismanagement, compliance failures, and inefficiencies in resource allocation. Traditional audit approaches often fail to detect systemic risks, leading to accountability gaps and financial irregularities. This study explores the implementation of Risk-Based Audit (RBA) Systems as a proactive approach to enhancing transparency, compliance, and financial integrity in affordable housing programs. RBA systems leverage data analytics, predictive modeling, and risk assessment frameworks to prioritize high-risk areas, ensuring that audit resources are strategically allocated. The research examines how RBA systems improve oversight by integrating real-time data analytics, fraud detection mechanisms, and machine learning models to identify anomalies and irregularities in housing fund disbursements. By applying key risk indicators (KRIs) and risk assessment matrices, auditors can focus on critical financial and operational risks, reducing the likelihood of misallocations and financial misreporting. The study also investigates the role of regulatory technology (RegTech) in automating compliance processes, improving audit efficiency, and minimizing manual errors in financial assessments. A comparative analysis of traditional audit methods and risk-based auditing in U.S. affordable housing programs is conducted, highlighting the effectiveness of predictive auditing frameworks. Case studies of successful RBA implementations in government-subsidized housing programs demonstrate improved accountability, reduced fraud, and enhanced financial sustainability. The findings emphasize that integrating artificial intelligence (AI)-driven risk detection and blockchain-based financial tracking strengthens audit mechanisms, ensuring that housing funds are utilized for their intended purposes. This research contributes to policy discussions on enhancing the governance of affordable housing programs by recommending a risk-tiered audit approach, where high-risk entities receive more frequent and intensive scrutiny. The study underscores the necessity of cross-agency collaboration, involving auditors, policymakers, and housing administrators, to establish a robust risk management culture. In conclusion, implementing Risk-Based Audit Systems is crucial for improving the accountability and financial integrity of U.S. affordable housing programs. Future research should explore the integration of quantum computing and advanced behavioral analytics to further enhance risk prediction models in housing finance audits.