Accurate forecasts of healthcare spending and financial risk sit underneath premium pricing, provider contracting, reserve setting, and capacity planning across U.S. insurers and hospital networks. The problem is harder than it sounds. Spending is heavy tailed, claims arrive late, benefit designs change, and diagnosis coding evolves, so naïve models either overfit last year’s noise or miss the tail where most dollars live. We synthesize findings from risk adjustment, health econometrics, and modern machine learning, and we propose an implementable framework for next year cost prediction and high cost risk stratification. The approach starts with contract aligned targets, uses feature windows that respect claims latency, and benchmarks interpretable two part and GLM baselines against gradient boosted trees and sequence neural models. Evaluation emphasizes both rank performance and calibrated dollars because calibration errors directly translate into mispricing and inequitable enrollment. We also specify governance controls, subgroup reporting, and security safeguards consistent with healthcare analytics and cybersecurity guidance (Hasan et al., 2022; Hasan et al., 2025; Milon et al., 2024). The manuscript provides practical tables and figures for deployment, including model choice guidance, metric selection, and monitoring for drift. The core takeaway is that predictive lift is real, but it is only valuable when targets match decisions, validation is temporal, and fairness is treated as a design constraint rather than an afterthought (Obermeyer et al., 2019). We emphasize interpretability artifacts that operations teams can act on, not just statistical significance. Deployment plans include monitoring for data shifts and rapid rollback procedures.