Epilepsy　is　a　chronic　central　nervous　system　disorder　characterized　by　recurrent　seizures.　Not　only　does　epilepsy　severely　affect　the　daily　life　of　the　patient,　but　the　risk　of　premature　death　in　patients　with　epilepsy　is　three　times　higher　than　that　of　the　normal　population.　Magnetoencephalography　(MEG)　is　a　non-invasive,　high　temporal　and　spatial　resolution　electrophysiological　data　that　provides　a　valid　basis　for　epilepsy　diagnosis,　and　used　in　clinical　practice　to　locate　epileptic　foci　in　patients　with　epilepsy.　It　has　been　shown　that　MEG　helps　to　identify　MRI-negative　epilepsy,　contributes　to　clinical　decision-making　in　recurrent　seizures　after　previous　epilepsy　surgery,　that　interictal　MEG　can　provide　additional　localization　information　than　scalp　EEG,　and　complete　excision　of　the　stimulation　area　defined　by　the　MEG　has　prognostic　significance　for　postoperative　seizure　control.　However,　due　to　the　complexity　of　the　MEG　signal,　it　is　often　difficult　to　identify　subtle　but　critical　changes　in　MEG　through　visual　inspection,　opening　up　an　important　area　of　research　for　biomedical　engineers　to　investigate　and　implement　intelligent　algorithms　for　epilepsy　recognition.　At　the　same　time,　the　use　of　manual　markers　requires　significant　time　and　labor　costs,　necessitating　the　development　and　use　of　computer-aided　diagnosis　(CAD)　systems　that　use　classifiers　to　automatically　identify　abnormal　activity.　In　this　review,　we　discuss　in　detail　the　results　of　applying　various　different　feature　extraction　methods　on　MEG　signals　with　different　classifiers　for　epilepsy　detection,　subtype　determination,　and　laterality　classification.　Finally,　we　also　briefly　look　at　the　prospects　of　using　MEG　for　epilepsy-assisted　localization　(spike　detection,　high-frequency　oscillation　detection)　due　to　the　unique　advantages　of　MEG　for　functional　area　localization　in　epilepsy,　and　discuss　the　limitation　of　current　research　status　and　suggestions　for　future　research.　Overall,　it　is　hoped　that　our　review　will　facilitate　the　reader　to　quickly　gain　a　general　understanding　of　the　problem　of　MEG-based　epilepsy　classification　and　provide　ideas　and　directions　for　subsequent　research.