Background: Migraine is a highly prevalent neurological disorder characterized by an unpredictable prodromal phase, during which subtle physiological and behavioral changes precede headache onset. Advances in digital phenotyping and artificial intelligence (AI) offer the potential to detect these changes through passive data collected from smartphones and wearable devices. Methods: This study investigated the feasibility of predicting migraine onset using machine learning models trained on digital behavioral biomarkers. A publicly available dataset comprising 11,879 daily observations from 100 users (January–August 2024) was analyzed. Features included sleep duration, screen time, stress, mood, hydration, and engineered temporal variables such as lag features, rolling averages, and composite risk scores. Logistic Regression, Random Forest, and Gradient Boosting models were developed and evaluated using ROC-AUC, average precision, precision, recall, and five-fold cross-validation. Additionally, a primary survey explored self-reported changes in typing speed and typing errors during the migraine prodromal phase. Results: All three models demonstrated predictive performance above chance, with Random Forest achieving the strongest overall performance (ROC-AUC = 0.674; accuracy = 61.8%). Temporal behavioral patterns were more informative than same-day measurements, with previous-day stress, sleep duration, and composite risk scores emerging as the most influential predictors. Survey findings indicated that approximately 29% of participants perceived slower typing before migraine onset, supporting typing dynamics as a potential digital biomarker, although most participants were unable to reliably recognize prodromal changes without objective monitoring. Conclusion: AI-based analysis of digital biomarkers can identify meaningful behavioral patterns preceding migraine attacks and may enable passive early-warning systems. Although current predictive accuracy is insufficient for standalone clinical use, integrating longitudinal digital phenotyping with explainable, personalized machine learning models could substantially improve proactive migraine management and reduce disease burden.