<p>China is under growing pressure from rising healthcare costs, persistent air-pollution exposure, rapid urbanization, and fast digital transformation. While previous studies have examined how pollution, energy production, and technological change affect health expenditure, most rely on average-based methods that overlook how these relationships differ when healthcare spending is low, moderate, or high. This study addresses that gap by treating health expenditure as a nonlinear, distribution-sensitive outcome shaped by artificial intelligence, green growth, energy production, and air quality. The study applies quantile ADF and quantile KPSS stationarity tests, and multivariate quantile-on-quantile regression, with conventional quantile regression used for robustness. The findings show clear nonlinear patterns, stable conditional distributions, and strong quantile-specific differences. Artificial intelligence has a mixed effect: it may increase spending through digital infrastructure and diagnostic investment, but may also reduce cost pressure through efficiency, early detection, and better resource allocation. For long-term policy, China’s health expenditure reflects a complex technology–energy–environment nexus, requiring coordinated policies on AI, green growth, clean energy, urban planning, and air quality improvement.</p> Graphical abstract <p></p>