Standard surgical resection of glioblastoma, mainly guided by the enhancement on post-contrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region. Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Non-invasive in vivo delineation of the areas of tumor infiltration and prediction of early recurrence in peritumoral edema region could assist in targeted intensification of local therapies, thereby potentially delaying recurrence and prolonging survival. This paper presents a method for estimating peritumoral edema infiltration using radiomic signatures determined via machine learning methods, and tests it on 90 patients with de novo glioblastoma. The generalizability of the proposed predictive model was evaluated via cross-validation in a discovery cohort (n=31), and was subsequently evaluated in a replication cohort (n=59). Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies. The cross-validated accuracy of our predictive infiltration model on the discovery and replication cohorts was 87.51% (odds ratio=10.22, sensitivity=80.65, specificity=87.63) and 89.54% (odds ratio=13.66, sensitivity=97.06, specificity = 76.73), respectively. The radiomic signature of the recurrent tumor region revealed higher vascularity and cellularity when compared with the nonrecurrent region. The proposed model shows evidence that multi-parametric pattern analysis from clinical MRI sequences can assist in in vivo estimation of the spatial extent and pattern of tumor recurrence in peritumoral edema, which may guide supratotal resection and/or intensification of postoperative radiation therapy.