This work presents a methodology for anticipating the emergence of fire events using hot spot products. It is based on the high temporal resolution and ultra-real-time availability of data from the ABI sensor of the geostationary satellites GOES-16 and GOES-19. The scope involves predicting the formation of events in Brazil and in the Amazon biome of the Amazon Cooperation Treaty Organization (ACTO) countries. It uses the K-Means algorithm for classifying clusters of recurrent alerts, formed by the spatio-temporal grouping of hot spot detections and considering the averages of: a) brightness temperature; b) estimated area; and c) radiative power. The data were processed through min-max normalization and the Euclidean distance from alerts to clusters was calculated. The differential of the approach lies in assigning a confidence estimate to each alert, indicating the probability that it anticipates the emergence of a fire event within up to 12 hours. The results obtained suggest that the methodology can contribute significantly to optimizing monitoring and directing actions, especially in remote regions, where early detection is crucial.