In this paper, we present a dynamic wildfire warning map that combines both spatial and weather information. In particular, our wildfire early warning model is obtained by aggregating two indexes called wildfire risk and wildfire danger. The wildfire risk index, which is based on georeferenced features such as altitude and forest type, measures the fuel necessary for a wildfire to start at a certain location on a map. The wildfire danger uses weather conditions to yield temporal information concerning the possibility of a wildfire to spread. Machine learning techniques and fuzzy logic operations are used to determine the wildfire risk and danger indexes from available data. Although both wildfire risk and wildfire danger indexes can be used separately, using concepts from fuzzy logic, they can be combined to yield a wildfire warning system that takes into account both weather and static information. We illustrate the wildfire early warning model by considering weather and geographical data for the state of Acre.