The emergence of artificial intelligence (AI) in the financial services sector, especially in investment banking, has drastically transformed the role of adherence. However, it also dangles unprecedented issues surrounding model risk governance (MRG). This paper assesses the impacts of the AI-based compliance systems management in the system of regulatory expectations and examines existing gaps in model validation practices. With the black-box nature of most machine learning (ML) models, more interest is raised about the lack of explainability, fairness, and accountability in these models, particularly with changing regulations such as Basel III and Dodd-Frank and U.S. regulatory reports, such as Y-14 and 2052a. The study can integrate regulatory compliance, AI governance, and operational risk through a multidisciplinary approach, thus building feasible tactics to enhance model risk governance. It includes the best validation practices, lifecycle management, and documentation. In this paper, we will seek to assist risk practitioners and compliance leaders in building more transparent, auditable, and regulation-compatible AI solutions.