In recent years, land uses have been changing and aridity has been increasing in the forests and rangelands of central Koohdasht which is a region in the forests of the Zagros Mountains in western Iran. Consequently, the number of fires has also increased. This study employs data-mining techniques to model fire danger using information regarding land cover, climate, topography, and other fire-danger influencing factors. A land cover map was prepared using Sentinel-2A satellite images and a maximum likelihood (ML) algorithm. Digital data describing other factors that influence fire danger (slope angle, aspect, elevation, climate, topographic wetness index, and distances from rivers and roads) were compiled from several sources and imported into a GIS. The locations of past fires in the study area were also determined from MODIS satellite images and data acquired from the region’s fire service. The quantitative and qualitative spatial relationships between effective factors and patterns of fires were investigated to model fire danger. A new machine-learning algorithm (the Boruta algorithm) was used to assess the relative importance of the fire-danger factors. Fire danger maps were created using several new data-mining algorithms including support vector machine (SVM), generalized linear model (GLM), functional data analysis (FDA), and random forest (RF). All were run in R 3.3.3 software. Finally, the fire danger maps were validated with several indices to determine the model that best predicts the fire danger in Koohdasht County. The results reveal that fire locations were determined mostly by elevation (low), aspect (south and southwest facing slopes), and aridity (semi-arid regions). Most fires occurred in non-natural landscapes: residential areas (46.74% of fires), agricultural lands (25.77%), and gardens (5.42%). In total, 77.93% of fires occurred in non-natural landscapes and within 500 m of roads. Only 22.07% of fires occurred on rangelands and forests. Three factors (distance from roads, climate, and aspect) were the strongest predictors of fire locations in the study area. Furthermore, area-under-the-curve (AUC) values indicate that the FDA (0.777) and GLM (0.772) algorithms generated the most accurate fire danger maps. These results have practical implications for fire danger management in the Zagros forests and provide baseline information for forest managers about the most important factors affecting fire danger in the similar regions. This methodology can be used by forest managers to predict the areas with greatest fire danger to prevent future fires through land use management, planning, and strategic decision-making. The results enable forest managers to find the best methods to monitor, manage, and control fire occurrence based on fire danger maps in the forests of western Iran, or in forests of other regions with similar conditions.