GreenChild is an AI-powered community health surveillance platform built to stop climate-driven malaria outbreaks before they kill children. The core insight: by the time a child arrives at a clinic with malaria, 5–10 children in the same compound already have symptoms. Mothers see this signal days earlier — but no system has ever captured it. GreenChild does. This poster walks through how Python and FastAPI power GreenChild's AI risk engine, which fuses mother-reported symptoms (collected via WhatsApp/USSD — no smartphone needed) with hyperlocal rainfall and temperature data from NASA's CHIRPS API to compute a real-time outbreak risk score per ward, updated every 24 hours. Key technical topics covered: - Designing a lightweight Python/FastAPI ML pipeline for low-resource environments - Integrating open climate datasets (NASA CHIRPS) with community-generated health signals - Building for USSD and WhatsApp — reaching mothers on basic 2G phones - Visualising geospatial health risk data with Leaflet.js The pilot targets 5 LGAs across Katsina and Kano — two of Nigeria's highest malaria burden states. The architecture is designed to scale to 10 million mothers across West and Central Africa.