Dengue fever presents a growing public health challenge in tropical and subtropical regions, where early detection is crucial for effective intervention. This study conducts a comprehensive comparative analysis of 13 machine learning and deep learning models for nonclinical, symptom‐based dengue prediction, focusing on the Bangladeshi population. Using a dataset of 500 patient records with 22 symptom‐based features, we evaluated a wide spectrum of classifier algorithms, including tree‐based (e.g., random forest, extra trees, bagging), linear (logistic regression, SGDClassifier), and an instance‐based classifier. Our comparative evaluation revealed that a custom‐built, hyperparameter‐tuned artificial neural network (ANN) achieved the highest accuracy of 97.5%, significantly outperforming all other models. While tree‐based models like random forest also demonstrated strong performance (93.2%), other classifiers showed considerably lower efficacy. To ensure transparency in our top‐performing model, SHapley Additive exPlanations (SHAP) was employed, identifying critical predictors such as retro‐ocular pain, swollen eyelids, and age. This study not only establishes the superiority of a well‐tuned ANN for this task but also demonstrates the value of broad model comparison and explainability in building reliable diagnostic tools for public health.