Healthcare fraud continues to plague the global healthcare industry, with estimated annual losses surpassing $350 billion in the U.S. alone. Beyond the economic damage, fraudulent activities undermine the quality of care, erode trust in healthcare systems, and jeopardize patient safety. Conventional methods of fraud detection, such as manual audits and rule-based systems, are increasingly ineffective against the growing sophistication of fraud schemes. Artificial Intelligence (AI), with its ability to process complex data, detect anomalies, and identify patterns, offers a paradigm shift in real-time fraud detection and prevention. This paper explores AI-driven solutions, including machine learning (ML), deep learning (DL), and predictive analytics, and their transformative impact on healthcare fraud management. The analysis highlights AI’s potential to enhance scalability, accuracy, and timeliness in detecting fraudulent activities. Furthermore, the paper addresses ethical considerations, such as bias and data privacy, advocating for transparent and responsible AI implementations. By leveraging AI, healthcare systems can transition from reactive to proactive fraud prevention, safeguarding resources and improving patient outcomes.