Neuroimaging studies are essential for evaluating patients with drug-resistant focal epilepsy and determining their candidacy for epilepsy surgery. The past decade has seen the emergence of neuroimaging-based deep learning models, which have been developed to both detect epileptogenic lesions on MR imaging and predict post-surgical seizure outcome. Large, multi-center studies have demonstrated promise for epileptogenic lesion detection; however, neuroimaging-based surgical outcome prediction models remain exploratory. Translation of such models into routine epilepsy surgery clinical workflows will require transparent, interpretable, and prospectively validated model designs.