Rising healthcare expenditure, unpredictable patient liabilities, insurance complexity, and fragmented financial data have increased the need for healthcare organizations to anticipate financial pressures rather than respond after costs and payment risks materialize. This study examines AI-powered predictive analytics in Health FinTech as a decision framework connecting healthcare cost management, financial risk assessment, and financial decision-making. The framework integrates historical expenditure, claims, payment behavior, insurance coverage, service utilization, and relevant socioeconomic indicators to generate prospective financial intelligence. Machine-learning models are applied conceptually to forecast treatment expenditure, identify cost escalation patterns, estimate patient payment risk, detect anomalous claims, and stratify financial vulnerability. These predictions inform decisions concerning budgeting, resource allocation, reimbursement management, payment scheduling, and targeted financial assistance. Particular emphasis is placed on transforming fragmented retrospective financial reporting into forward-looking decision support for patients, providers, insurers, and financial institutions. The study further addresses model explainability, data quality, algorithmic bias, privacy, and prediction uncertainty as determinants of responsible deployment. Integrating predictive intelligence with Health FinTech can enable earlier financial intervention, more precise cost management, risk-sensitive financing, and evidence-driven healthcare financial decisions.