This study advances forest fire susceptibility mapping in Gia Lai province by leveraging optimized machine learning models. We evaluated five models -Deep Neural Networks (DNN), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and Support Vector Machines (SVM) - using a dataset of 2,827 fire incidents (2007÷2021), an equal number of non-fire points, and 12 influencing factors: slope, aspect, elevation, curvature, land use, NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDMI (Normalized Difference Moisture Index), temperature, wind speed, relative humidity, and rainfall.