Patients presenting with drug-resistant epilepsy are eligible for surgery aiming to remove the regions involved in the production of seizure activities, the so-called epileptogenic zone network (EZN). Thus the accurate estimation of the EZN is crucial. Data-driven, personalized virtual brain models derived from patient-specific anatomical and functional data are used in Virtual Epileptic Patient (VEP) to estimate the EZN via optimization methods from Bayesian inference. The Bayesian inference approach used in previous VEP integrates priors, based on the features of stereotactic-electroencephalography (SEEG) seizures’ recordings. Here, we propose new priors, based on quantitative 23 Na-MRI. The 23 Na-MRI data were acquired at 7T and provided several features characterizing the sodium signal decay. The hypothesis is that the sodium features are biomarkers of neuronal excitability related to the EZN and will add additional information to VEP estimation. In this paper, we first proposed the mapping from 23 Na-MRI features to predict the EZN via a machine learning approach. Then, we exploited these predictions as priors in the VEP pipeline. The statistical results demonstrated that compared with the results from current VEP, the result from VEP based on 23 Na-MRI prior has better balanced accuracy, and the similar weighted harmonic mean of the precision and recall. Author Summary For the first time quantitative 23 Na-MRI were used as prior information to improve estimation of epileptogenic network (EZN) using VEP pipeline, a personalized whole-brain network modeling from patient’s specific data. The prior information of EZN can be derived from 23 Na-MRI features using logistic regression predictions. The 23 Na-MRI priors inferred EZNs has a better balanced accuracy than the previously used priors or the no-prior condition.