INTRODUCTION: An elevated Ki-67 is one crucial factor that influences meningioma behavior. Machine learning(ML) using radiomic feature analysis can identify phenotypic pixel-level imaging signatures for enhanced tumor diagnostics. METHODS: Radiomic feature extraction using least-absolute-shrinkage-and-selection-operator(LASSO) wrapped with support vector machine (SVM) through nested cross-validation is used to stratify tumors based on Ki-67 <5% and ≥5%. Clinical outcomes were evaluated based on the predictive power of this algorithm. RESULTS: 343 patients are included (WHO grade I:291, grade II:43, grade III:9). Overall mean follow-up time is 33.9 months (range: 0-105). The rate of recurrence is 14.4%, 44.2% and 77.8% for grade I, II, and III tumors, respectively. The mean Ki-67% for grade 1, 2 and 3 meningiomas is 4.79+/-3.87 (range: 0.3-33.6), 16.07+/-13.83 (range: 1.5-49), and 35.7+/-13.3 (range: 18-57.4), respectively (p = 0.03). However, there is no difference in tumor and peritumoral edema volumes between meningioma WHO grades 1-3. A total of 46 high-ranking radiomic features were selected to build a ML model. ROC curves for the ML algorithm reveals AUC’s of 0.83 95% CI: 0.78-0.89 and 0.84 0.75-0.94 for the discovery (N = 257) and validation (N = 86) cohort, respectively, for classifying Ki-67% based on a 5% cutoff, independent of WHO grade. Kaplan-Meier curves using the ML algorithm reveals decreased progression-free-survival (PFS) for predicted Ki-67 >5% (p < 0.001) for all tumors in the cohort, as well as in sub-analyses of non-skull base tumors (p < 0.001) and skull-base tumors (p < 0.001). A comparison of histopathological PFS versus ML-predicted outcomes based on Ki-67 reveals good concordance for grade I, II and III tumors. CONCLUSIONS: ML using radiomic feature analysis can be used to stratify meningiomas based on Ki-67 with excellent accuracy. The predictive power of this model reveals distinctly divergent PFS outcomes, and can be used to guide treatment strategy.