Healthcare insurance fraud is a growing concern, costing insurers billions of dollars and straining healthcare systems worldwide. Traditional methods, like manual claim reviews and rule-based detection, struggle to keep up with the increasingly sophisticated tactics used by fraudsters. This paper explores how artificial intelligence (AI) is transforming fraud detection, offering smarter and faster ways to identify suspicious claims. By leveraging machine learning and deep learning, AI can analyze vast amounts of data, spot hidden patterns, and adapt to new fraud schemes more effectively than ever before. We examine real-world examples of AI-driven fraud detection, highlighting its successes while also addressing key challenges such as data privacy, ethical concerns, and algorithmic bias. Looking ahead, advancements in AI, blockchain, and predictive analytics promise even greater efficiency in fraud prevention. This study provides insights for researchers and industry professionals seeking to harness AI’s potential to safeguard healthcare insurance from fraud.