Healthcare fraud continues to be a significant challenge worldwide, resulting in severe financial losses and deterioration in patient care. In recent years, the application of machine learning (ML) techniques has shown to be a viable method for identifying and stopping fraud in healthcare systems. This paper presents a comprehensive review of the existing literature on healthcare fraud detection using ML techniques. The review begins by discussing the various types and common schemes of healthcare fraud, highlighting the complexity and diversity of fraudulent behaviors observed in medical billing, insurance claims, and prescription practices. Subsequently, it provides an overview of the fundamental concepts and methodologies of ML, emphasizing their relevance and applicability in the context of healthcare fraud detection. The main body of the review categorizes and analyzes the state-of-the-art ML approaches employed for healthcare fraud detection, including anomaly detection, predictive modeling, network analysis, behavioral profiling, and ensemble techniques. Each category is explored in detail, discussing the strengths, limitations, and key findings of relevant studies, along with examples of real-world applications and case studies. Furthermore, the review identifies current challenges and open research questions in healthcare fraud detection using ML techniques, such as the need for robust feature engineering methods, the scarcity of labeled fraudulent data, the interpretability of ML models, and the scalability of fraud detection systems in real-time settings. Finally, the paper concludes with a discussion of future directions and emerging trends in healthcare fraud detection research, including the adoption of advanced ML algorithms, the integration of multiple data sources, the exploration of privacy-preserving techniques, and the development of collaborative frameworks for cross-institutional fraud detection efforts. Overall, this review provides valuable insights into the state-of-the-art techniques, challenges, and opportunities in healthcare fraud detection using machine learning, serving as a reference for researchers, practitioners, and policymakers aiming to combat fraud and enhance the integrity of healthcare systems.