The integration of Artificial Intelligence (AI) into healthcare revenue cycle management (RCM) systems is revolutionizing compliance automation, audit readiness, and fraud detection across the healthcare enterprise. This review explores how AI-driven compliance automation frameworks leverage machine learning (ML), natural language processing (NLP), and robotic process automation (RPA) to ensure real-time regulatory adherence, minimize billing anomalies, and enhance audit transparency. By analyzing data across claim submissions, coding accuracy, denial management, and payment reconciliation,AI systems enable predictive risk scoring and anomaly detection to identify irregular claim patterns indicative of fraudulent activities or noncompliance. Furthermore, explainable AI (XAI) models are increasingly used to provide interpretability in compliance decision pathways, supporting auditors in tracing logic-based evidence trails during regulatory reviews. The study also examines the role of generative AI in automating documentation compliance, particularly in aligning electronic health records (EHRs) with Centers for Medicare & Medicaid Services (CMS) and Health Insurance Portability and AccountabilityAct (HIPAA) standards. A comparative assessment of legacy compliance models versus AI-augmented systems highlights significant reductions in falsepositive fraud alerts, audit preparation time, and operational overhead. This paper highlightss the convergence of AI analytics, data governance frameworks, and healthcare informatics in shaping a resilient, transparent, and fraud-resilient RCM ecosystem. Future research directions include the standardization of AI auditing protocols, ethical governance in algorithmic decision-making, and the integration of federated learning for privacy-preserving fraud analytics across multi-institutional datasets.