Focal cortical dysplasia (FCD) is a common pathology underlying medically intractable focal epilepsies. Conventional MRI can be limited in characterizing subtle FCD, due to the lack of quantitative measurements for tissue properties. Here, we proposed a multiparametric machine learning (ML) approach based on high-resolution 3D MR fingerprinting (MRF) to characterize FCD lesions. The ML model showed robust accuracy of 96%, 89%, and 79% to separate FCD from normal cortex, FCD type I from type II, and FCD type IIa from type IIb, respectively. Our findings suggest the usefulness of the multiparametric MRF ML approach to improve noninvasive epilepsy presurgical evaluation.