This article analyzes state budget financing in the healthcare sector of the Republic of Uzbekistan and introduces an applied Information Technology (IT) component to support future healthcare financing decisions. As Uzbekistan continues its transition toward state health insurance and modern reimbursement models such as capitation, data-driven decision-support systems may complement traditional budget analysis. To support this transition, we developed a Machine Learning (ML) binary classification model to predict citizens’ Willingness-to-Pay (WTP) for the upcoming mandatory medical insurance system. Using a structured digital survey dataset, we compared a Logistic Regression baseline with a Random Forest Classifier. The Random Forest model outperformed the baseline, achieving 75% accuracy and 0.92 recall for the positive class. Feature importance analysis showed that historical out-of-pocket medical expenses and perceptions of insurance benefits were stronger predictors of WTP than standard demographic variables. These pilot findings suggest that predictive ML models may support ongoing and future healthcare financing reforms in Uzbekistan by helping policymakers design more targeted awareness campaigns and subsidy strategies. However, the model should be interpreted as a pilot decision-support tool and not as a direct evaluation of earlier budget decisions.