Climate change is amplifying extreme events such as heatwaves, floods, and storms. While current EWS focus on infectious diseases and injuries, neurological health, encompassing stroke, epilepsy, neurodegenerative and neuroinfectious diseases, remains largely absent from climate adaptation strategies. Evidence links compound exposures (e.g., heat with air pollution) to increased stroke and seizure risk, yet predictive modeling for neurological outcomes is underdeveloped. Although AI and machine learning have advanced infectious disease forecasting, their application to neurology is limited by fragmented datasets, lack of standardized indicators, and minimal integration into surveillance systems.

We propose a four-pillar AI-enabled framework for integrating neurological health into climate preparedness:

1. Predictive Modeling: Leverage AI, satellite data, and compound exposure analytics to forecast acute neurological events (e.g., stroke, seizure surges) with temporal lags.
2. Syndromic Surveillance Integration: Embed neurological syndromes into real-time surveillance platforms, enhanced with AI signal detection and environmental–neurological data linkage.
3. Neurology-Centered Disaster Preparedness: Develop protocols for medication continuity, neurodegenerative care, seizure response, and infrastructure resilience during climate emergencies.
4. Interdisciplinary Data and Policy Infrastructure: Standardize neurological indicators, strengthen data-sharing systems, and embed neurological expertise within climate-health governance frameworks globally.

Integrating AI-driven predictive modeling and surveillance with neurology can transform climate adaptation strategies from reactive to anticipatory. This framework addresses critical gaps in predictive analytics, surveillance integration, preparedness, and policy, positioning neurological health as a core component of global climate resilience.