Epilepsy is a common neurological disorder, but its diagnosis remains difficult when screening EEGs lack interictal epileptiform discharges (IEDs). Intermittent photic stimulation (IPS) can reveal abnormal responses associated with epilepsy; however, its clinical interpretation is often subjective, inconsistent, and sometimes inconclusive. This thesis explores the automatic classification of EEG responses to IPS using machine learning to improve diagnostic accuracy and reliability. Two datasets are analysed: the Temple University Hospital (TUH) Epilepsy Corpus and clinical recordings from Erasmus MC. A structured pipeline is developed, comprising preprocessing, feature extraction across temporal, spectral, wavelet, and connectivity domains, and classification with interpretable models such as XGBoost and ensemble approaches. To ensure robust generalization, leave-one-subject-out cross-validation is employed. This work demonstrates that IPS EEG segments contain informative features capable of distinguishing epileptic from nonepileptic patients, even in the absence of IEDs, thereby aiding early diagnosis and reducing the risk of misdiagnosis. Furthermore, the use of explainability tools highlights candidate electrophysiological markers, providing valuable insights and suggesting new hypotheses for future investigation.