In 2018, nine Central American and Caribbean countries joined the Regional Malaria Elimination Initiative (RMEI). Since then, only two have eliminated malaria, while most have seen case increases. To support elimination efforts, researchers created a Malaria Early Warning System (MEWS) for Panama and Honduras. This system combines data on malaria cases, climate, and land use to help predict and visualize malaria trends at the community level. It uses advanced machine learning and statistical models to forecast malaria cases up to nine months in advance. The system has demonstrated strong accuracy, with an average error of less than 7% and even better performance in recent forecasts. MEWS can also forecast specific malaria types and differentiate between new and recurring cases, tailored to country-specific needs. However, the accuracy depends on the quality of local health data. The presentation discusses the development of MEWS, its performance, and the challenges associated with its use. It also explores expanding the system Ito other RMEI countries, including early testing in Colombia and future plans for Guatemala.