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The rising prevalence of cardiometabolic multimorbidity (CMM), characterized by the coexistence of two or more cardiometabolic disorders, poses a significant public health challenge in aging populations. While ambient air pollution is a recognized environmental risk factor, its long-term impact on CMM remains underexplored, particularly in China. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS, 2015 wave), we analyzed 9,830 participants aged ≥ 45 years. CMM was defined as the concurrent presence of two or more conditions: diabetes/hyperglycemia, cardiovascular diseases (myocardial infarction, angina pectoris, coronary artery disease, or heart failure, or stroke. Annual pollutant exposures were estimated using a machine learning-based spatiotemporal model (space-time extremely randomized trees) based on residential addresses. Generalized linear models adjusted for sociodemographic, lifestyle, and meteorological covariates were employed to assess odds ratios (ORs) per interquartile range (IQR) increase in pollutants. Chronic exposure to particulate matter (PM) 10 demonstrated a consistent positive association with CMM prevalence across models. In fully adjusted analyses, each IQR increase in PM10 was associated with elevated CMM risk (OR = 1.01, 95% CI: 1.00-1.02, P = 0.039). Sensitivity analyses, including alternative exposure windows and adjustments for regional variations, reinforced PM10's robust association with CMM. Other pollutants (PM2.5, and sulfur dioxide) showed weaker or inconsistent associations. Long-term exposure to ambient particulate matter, particularly PM10, is significantly linked to increased CMM prevalence in China's aging population. The findings of this study provide epidemiological evidence, laying the foundation for future cohort studies and mechanistic investigations.