Ambient air pollution remains a major, preventable driver of cardio metabolic and neurological disease burden. For biomedical studies, the central methodological bottleneck is not only prediction of pollutant concentrations, but trustworthy exposure assessment: leakage-safe validation, Uncertainty Quantification (UQ), transportable models in low-monitor regions, and transparent propagation of exposure uncertainty into health-effect estimates. This mini-review synthesizes recent advances in global and regional PM2.5 mapping, spatiotemporal deep learning, virtual monitoring stations, and gap-filling, and links these developments to the rapidly expanding evidence on dementia risk. We provide a practical checklist and worked calculations that translate modern Machine Learning (ML) exposure products into epidemiology-ready inputs.