Liyanage, DVithanage, NWijewardane, IFernando, NWijendra, DDassanayake, T2026-05-252026-03-1927681831https://rda.sliit.lk/handle/123456789/5048Air pollution threatens public health in datascarce urban areas like Sri Lanka, where sparse monitoring hinders proactive management. We propose an integrated AI framework: hybrid SARIMAX-Temporal Fusion Transformer for multi-pollutant forecasting, ensemble spatial estimation for gap-filling, CEEMDAN-Seq2Seq for 24-hour AQI risk alerting, GRU for anomaly detection, and XAI for transparency. Validated on Central Environmental Authority data (20192024), the model achieves an 81.6% decrease in the value of the RMSE metric for ozone forecasting, as well as an R2 value of 0.9077 for high-risk AQI prediction, outperforming the baseline methods by 15-81%. The framework is modular in nature, thereby providing policymakers with the ability to use real-time dashboards, thus making Sri Lanka move from reactive to proactive management.enair quality managementanomaly detectionensemble spatial estimationExplainable AI (XAI)hybrid deep learningrisk assessmentArticleDOI: 10.1109/ISDFS69419.2026.11459026