Dengue fever is a mosquito-borne viral infection causing significant public health problems, especially in the tropics and subtropics. Traditional diagnostic tests such as serological tests and viral isolation are not affordable, requiring expensive equipment and reagents, and thus cannot be accessed in most rural areas. The capability of several machine learning techniques—bagging, boosting, and deep learning models—is evaluated for the non-invasive diagnostic identification of dengue in patients using clinical record data on symptoms and demographic information. The dataset comprised 1241 clinical records and features that included demographic and symptomatic details of patients. Post-preprocessing, which included synthetic oversampling, feature selection, and encoding categorical variables, the data was applied to train and test on multiple models. The results showed that bagging models, specifically the Random Forest and Extra Trees Classifier, had the highest accuracy values at 100% and 99.67%, respectively. The boosting models, such as CatBoost, performed quite well at 99.53% accuracy. Deep learning models were a mixed bag; those that reached 99.69% accuracy took a long time to compute. These models will prove useful as an inexpensive, practical substitute for real-time diagnosis of dengue in places where laboratory testing is limited, thereby improving timely diagnosis and treatment. Among the models, CatBoost was observed to strike a good balance between accuracy and speed which makes it ideal for use in resource-poor settings. The future work lies in the validation of such models with diverse datasets to establish broader applicability.