In order to scientifically assess the susceptibility to geological disasters in tropical counties, 12 evaluation factors covering topographic features, geological conditions, human engineering activities, and natural environments were selected in this study. FR (frequency ratio) method was applied to quantify these factors, and the quantified factors were used as input variables to be integrated with three machine learning models, namely RF (random forest), LR (logistic regression), and SVM (support vector machine). Through this integration, three hybrid models (FR-RF, FR-LR, and FR-SVM) were developed. These models were employed to evaluate the geological disaster susceptibility in Qiongzhong County, Hainan Province.The results show that high-susceptibility zones are mainly distributed in mountainous and gully areas, which are characterized by intense topographic relief and significant hydraulic erosion. AUC (area under curve) values of the three models are 0.95 for FR-SVM, 0.94 for FR-RF, and 0.79 for FR-LR, all of which exceed 0.7. Among them, the FR-SVM and FR-RF models exhibit superior predictive performance. Further comparative analysis based on precision, accuracy, recall, and F₁ score demonstrates that the FR-RF model achieves the best overall performance. It is concluded that this study effectively improves the accuracy of geological disaster prediction under small-sample conditions. The integration of the frequency ratio method with machine learning algorithms provides a reliable technical basis for disaster prevention, mitigation, and planning in Qiongzhong County, as well as in other similar tropical regions.