Advancing Air Pollution Exposure Science Using GeoAI: A Review of Progress, Challenges, and Future Perspectives

ABSTRACT Traditional air pollution exposure assessment relies on sparse monitoring networks, leading to significant spatiotemporal gaps and exposure misclassification. Geospatial Artificial Intelligence addresses these limitations by integrating multisource data with advanced algorithms to generate high‐resolution, seamless pollution maps. This review particularly highlights GeoAI's role in enabling the paradigm shift from static to dynamic, person‐centric exposure assessment, providing the first comprehensive synthesis of technical challenges and emerging solutions critical for real‐world implementation. Core applications include high‐resolution pollution mapping, dynamic exposure estimation and health impact assessment, environmental justice analysis, and air quality forecasting and early warning systems. We discuss significant challenges in data management, modeling methodology, and implementation, alongside potential solutions including explainable AI and uncertainty quantification. Ultimately, GeoAI drives a fundamental transformation from static, place‐centric assessments to dynamic, person‐centric models, enabling more precise and equitable environmental health research and governance.