Background <p>To explore the relationship between relaxed T2-FLAIR mismatch (RT2FM) sign, fractal dimension (FD) of tumor contour and IDH mutation status, and construct models for IDH mutation prediction in non-enhancing gliomas.</p> Methods <p>This retrospective study enrolled 364 patients with non-enhancing gliomas from two independent cohorts: cohort A (<i>n</i> = 267) for training &amp; internal validation set and cohort B (<i>n</i> = 97) for external testing set. RT2FM, FD, and other MRI semantic features were extracted. Boruta and least absolute shrinkage and selection operator algorithms were employed to select intersecting features. Four machine learning models were constructed using the intersecting features and their diagnostic performance was evaluated.</p> Results <p>The RT2FM sign predicted IDH mutation with an accuracy, sensitivity, and specificity of 0.969, 0.650, and 0.921 in cohort A, and 0.924, 0.419, and 0.762 in cohort B, respectively. In cohort A, FD were significantly higher in IDH wild-type than in IDH mutant groups (1.264 vs. 1.190; <i>P</i> &lt; 0.001). Using an FD cutoff value of 1.225, the area under the curve (AUC) and accuracy for predicting IDH mutation were 0.884 and 0.839, respectively. Among models constructed using four intersecting features (FD, RT2FM, multifocal/multicentric, and tumor location), XGBoost demonstrated the optimal predictive performance, with AUCs of 0.974, 0.968 and 0.895 in the training, internal validation and external testing set, respectively.</p> Conclusions <p>RT2FM and FD can provide informative imaging biomarkers for predicting IDH mutation. The XGBoost model constructed by these features demonstrated favorable diagnostic performance for IDH mutation prediction in non-enhancing gliomas.</p> Clinical trial number <p>Not applicable.</p>