Objective: This study explores how to utilize machine learning technology to enhance the identification accuracy and regulatory efficiency of medical insurance violations, given the new challenges faced by medical insurance fund supervision, in order to promote the stability and sustainable development of the fund. Methods: To effectively identify complex and concealed patterns of medical insurance violations, this study adopts a hierarchical analysis method based on machine learning, processes and integrates structured and unstructured medical insurance data from patients with three high-volume disease entities (breast malignancy, coronary heart disease, and acute pancreatitis) in a tertiary hospital in City C, Sichuan Province from 2023 to 2024, and analyzes potential risks of medical insurance violations. Results: Risk outliers are highly concentrated in cross-groupings of specific ages and lengths of hospital stays, accurately identifying high-risk populations that require key monitoring. The abnormal cost structure exhibits two differentiated patterns: high proportion (indicating overtreatment) and low proportion (indicating insufficient service), revealing the concealment and dynamic adaptability of medical insurance violations. This method provides an effective technical path for constructing an intelligent monitoring system, implementing precise risk warnings, and promoting the transformation of medical insurance governance models toward prior warning and in-process intervention. Conclusion: This study verifies the effectiveness and application value of the hierarchical analysis method combined with machine learning technology in accurately identifying potential risks of medical insurance violations. In the future, efforts should be made to promote cross-departmental data governance and deep integration, continuously optimize the interpretability and adaptability of algorithm models, strengthen the construction of composite talent teams, and closely integrate with payment method reforms such as DRG/DIP, iteratively build a more intelligent, efficient, and robust comprehensive governance system for medical insurance funds. This will systematically strengthen the safety defense line of the fund and promote the high-quality and sustainable development of the medical security cause.