Persistent growth in the scale and complexity of financial fraud continues to challenge the effectiveness of traditional audit methodologies and threatens confidence in U.S. capital markets. This study provides a systematic review of academic and professional literature examining the role of artificial intelligence (AI) and blockchain technologies in financial auditing, with particular emphasis on fraud detection mechanisms and their implications for audit quality and investor confidence. The review synthesizes evidence on AI-based techniques including machine learning, deep learning, natural language processing, and predictive analytics that enhance anomaly detection, enable continuous auditing, and improve risk assessment in complex financial environments. Findings from this study indicate that the joint application of AI and blockchain constitutes a complementary and reinforcing audit architecture in which advanced analytical intelligence is supported by secure, tamper-resistant data infrastructure. This integration improves audit efficiency, accuracy, and timeliness while reducing information asymmetry between firms and capital market participants. However, the review also identifies significant barriers to adoption, including data privacy and cybersecurity concerns, algorithmic opacity, skills shortages within the audit profession, scalability constraints, and regulatory uncertainty. Overall, the evidence positions AI and blockchain-enabled auditing as a structural shift from retrospective assurance toward proactive, continuous, and risk-oriented audit models. The study contributes to the accounting and finance literature by clarifying the mechanisms through which emerging technologies influence fraud detection and investor trust, and by outlining governance and regulatory priorities necessary for their responsible integration into the audit function.