Migraine is a debilitating neurological disorder affecting millions of people worldwide; it has several forms, each requiring an accurate diagnosis for effective treatment. This work investigates the performance of various machine learning models in classifying migraine subtypes based on extensive clinical features. Herein, we utilized K-Nearest Neighbors (KNN), Decision Trees, MLP Classifiers, Support Vector Machines (SVM), Random Forests, Deep Neural Networks (DNN), and GridSearchCV-optimized versions of MLP and SVC. All the performance parameters, such as accuracy, precision, recall, and F1-score of the different models, are measured on a standardized dataset that includes features of a migraine. These will help us to find, through a great deal of experimentation, the most accurate and interpretable model for classifying migraines. Our results shall provide important insights into methods for the better diagnosis and hence treatment planning of migraineurs.