Healthcare fraud, waste, and abuse (FWA) are among the most significant drivers of financial inefficiency in healthcare systems worldwide, with estimates suggesting they contribute to as much as 10% of total healthcare expenditures. In the United States, these losses are estimated to exceed $300 billion annually. This paper presents a machine learning approach using Isolation Forest, an unsupervised anomaly detection algorithm, to proactively identify potential FWA in healthcare claims data. Based on real-world patterns, a synthetically generated dataset was created to simulate both legitimate and fraudulent billing behavior. The model achieved high performance with a precision of 0.81, recall of 0.82, F1 score of 0.81, and ROC-AUC of 0.90. Through case studies and comparison of performance with baseline models, we demonstrate the practical applicability of this approach in real-time fraud monitoring. The study also discusses key implementation considerations, including ethical and regulatory factors, explainability, and system integration. We argue that intelligent anomaly detection models can be integrated with payer systems to improve financial stability and healthcare affordability.