Migraine, a debilitating neurological disorder affecting millions worldwide, presents diverse subtypes requiring accurate diagnosis for effective treatment. This study investigates the efficacy of machine learning models in classifying migraine subtypes based on comprehensive clinical attributes. We employ Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Decision Trees, MLP Classifiers, Support Vector Machines (SVM), Random Forests, and optimized versions of MLP and SVC via GridSearchCV. Utilizing a standardized dataset encompassing various migraine characteristics, we comprehensively compare and evaluate each model’s performance metrics, including accuracy, precision, recall, and F1-score. Through extensive experimentation, this analysis aims to identify the most accurate and interpretable model for migraine classification. Our findings contribute valuable insights to enhance healthcare practices by enabling improved diagnosis and personalized treatment strategies for migraineurs.