This study aims to evaluate the developed predictive dropout risk model in the Alternative Learning System (ALS) by analyzing various demographic, socio-economic, academic, and behavioral factors. The early identification of students who are at risks in dropping out is crucial in order to provide necessary academic intervention programs. The researcher used Knowledge Discovery in Databases (KDD) as methodology in the evaluation of the predictive models. Using Gradient Boosting Decision Trees (GBDT) for predictive modeling. Key findings highlighted that with both classes achieving an F1-score of 93% which demonstrate a balanced performance between precision and recall for both positive and negative classes. In summary, the overall evaluation of the system is 3.59 which indicates that they system can be used for deployment and maybe further be improved.