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The accurate determination of genotype and the clinical outlook for individuals with glioma present notable challenges, frequently relying on subjective evaluations and minimal scientific backing. This investigation intends to establish a dependable, noninvasive, preoperative learning framework based on a multi-modal MRI transformer to forecast IDH genotyping alongside glioma outcomes. This multi-institutional investigation gathered 563 individuals with glioma to construct an interpretable categorization model that leverages diverse preoperative imaging tracks, such as T1-weighted, T2-weighted, fluid-attenuated inversion recovery, contrast-enhanced T1-weighted, and diffusion-weighted imaging. The system utilizes a multi-task learning architecture designed to isolate and merge radiomic, deep learning, and clinical variables for IDH genotyping and glioma prognosis. Furthermore, a multi-modal transformer technique is incorporated to evaluate anatomical and functional MRI scans, thereby boosting the capabilities of the model. Empirical outcomes demonstrate that the framework achieves exceptional capability, outperforming prior investigations and alternative state-of-the-art approaches. The model attains an AUC of 91.40% for the IDH genotyping objective and 93.37% for the glioma prognosis objective. Subgroup evaluations indicate that the system displays heightened sensitivity toward IDH-mutant instances and identifies low-risk cohorts with greater precision than medium- or high-risk cohorts. This investigation intends to achieve precise IDH genotyping and glioma prognosis through an efficient classification methodology, delivering helpful diagnostic perspectives for clinical workflows and accelerating therapeutic determinations.