Dengue virus (DENV) infection is a significant public health concern in Indonesia, with increasing cases and severity posing challenges to the country’s healthcare systems. This study aims to develop and validate a machine learning-based prediction model for assessing dengue infection cases and their severity. The model incorporates epidemiological, clinical, and environmental factors to enhance early detection and resource allocation. Additionally, the model can be utilized to support logistics planning, such as the distribution of diagnostic kits and the preparation of health facilities in each region across Indonesia, ensuring timely and targeted responses to potential outbreaks. We applied various machine learning algorithms, including logistic regression, random forest, XGBoost, and SVM models, and evaluated them to determine the most effective predictive model. The results demonstrate the model’s efficacy in predicting dengue cases and severity, which can support public health interventions and clinical decision-making. Geospatial clustering and correlation matrices were generated to visualize risk patterns and support predictions. The XGBoost model demonstrated the highest performance, achieving an accuracy of 85%. Our findings suggest that integrating clinical and environmental data through machine learning (ML) techniques can significantly improve early detection and inform resource allocation strategies. The model offers a promising approach for public health surveillance and targeted interventions in dengue-endemic regions.