The evaluation of health systems is crucial at both macro and micro levels. At the macro level, it facilitates international comparisons, monitors progress towards global health goals, guides policymaking, and ensures effective resource allocation in public health. At the micro level, it allows countries to measure the effectiveness of their health interventions, identify health trends, and pinpoint areas needing improvement. A health system involves a wide range of stakeholders, including organizations, institutions, resources, patients, and potential patients, all working together to deliver health services to a population. In this study, health indicators from 30 OECD (Organization for Economic Cooperation and Development) countries were collected from 2006 to 2020 and evaluated using both supervised and unsupervised machine learning methodologies. The findings, demonstrating high accuracy in machine learning methods (exceeding 93.3%), indicate that financial models alone are inadequate for evaluating healthcare systems. The complexity and multidimensional nature of these systems necessitate the inclusion of health indicators in the evaluation process. Even when sufficient financial resources are allocated, without effective and implementable health policies based on the core principles of effectiveness, efficiency, equity, and quality of service delivery, desired health outcomes are not always achieved. By employing unsupervised machine learning methodologies to analyze health indicators, this study offers policymakers, researchers, and stakeholders deeper insights into the characteristics and performance of different healthcare systems. These insights can inform policy decisions, facilitate international comparisons, and contribute to the continuous improvement of healthcare systems globally.