Myanmar faces a significant malaria challenge, characterized by the presence of artemisinin-resistant Plasmodium falciparum parasites. Between 2012 and 2018, the country’s malaria elimination program successfully reduced cases by 82%. Despite this progress, malaria hotspots persist, particularly in remote rural regions that are hard to monitor. To enhance elimination efforts, the Malaria Map Early Warning System (MMEWS) has been established. This system assesses malaria risk through a conceptual framework that combines environmental factors influencing vector populations with socio-demographic and public health elements affecting human exposure and vulnerability. MMEWS models malaria risk spatially, focusing on three components: hazard (the prevalence of malarial parasites / vectors), exposure (human interactions with the landscape that expose them to malarial parasites), and vulnerability (factors that make some people more likely to get the disease). MMEWS model thus uses data fusion to provide spatially explicit MBP and identifies emerging hotspots with an 8-day reporting frequency. It predicts malaria outbreak locations and timings several months in advance by integrating satellite data with public health and socio-economic information, targeting the general population to assist in combating malaria in Myanmar.