Unmanned aerial vehicle (UAV) systems are widely used in many forest-related fields owing to their cost-intensive and precise surveying technology. In this study, we classified erosion susceptibility (ES) in a timber harvesting area using machine learning (ML) and statistical approaches. In dataset generation for the training and testing processes, the digital surface model (DSM) of difference (DoD) for July and June 2022 was used as a dependent variable, and six terrain maps of the DSM for June were used as independent variables. The ES threshold was set at 5 cm for the binary classification of ES pixels while processing using ML [e.g., random forest and extra gradient boost (XGB)] and statistical (e.g., logistic regression) algorithms for model development. The overall accuracy (OA), receiver operating characteristics, and area under the curve (AUC) were calculated for model accuracy and validation. Although the AUC of all the models did not appear acceptable (AUC > 0.7), the XGB model showed the highest performance in terms of time duration, OA, and AUC of 2 h, 64%, and 0.63, respectively. Despite the low AUC and accuracy of the XGB model, the wheel tracks and edges of the operation road were determined to be erosion-susceptible areas in the ES map of the XGB model.