Diffuse, or infiltrative, gliomas are Central Nervous System (CNS) brain tumors that account for 30% of brain and CNS tumors and 80% of brain tumors. Gliomas originate from the supportive glial cells: oligodendrocytes, astrocytes, and ependymal cells. This broad range of glial cells causes different forms of gliomas such as Lower-grade gliomas and Glioblastoma multiforme (GBM). GBM accounts for 14.6% of primary brain and CNS tumors, 48.3% of primary malignant brain tumors, and 57.3% of gliomas. GBM is staged as a grade IV tumor that arises from astrocytes glial cells. The extensive infiltrative growth pattern of Glioblastoma makes the curative treatment impossible and hence, reduces the median survival rate to 15 – 16 months. In 2016, the World Health Organization (WHO) classification of the Central Nervous System presents a major restructuring of diffuse gliomas. The updated classification of diffuse gliomas group tumors is based on the shared genetic driver mutations (e.g., IDH mutations and 1p/19q co-deletion), in addition to their growth pattern and behavior. Currently, the status of molecular mutations is determined by obtaining tissue samples that represent enough regions of the tumor with elevated proliferation and neovascularization. Tissue sampling might cause infections that require hospitalization. Consequently, a shift into developing non-invasive methods that analyze and predict clinical outcome (such as survival analysis and prediction, tumor aggressiveness, and molecular grading) of diffuse gliomas has encouraged researchers to exploit Radiomics and machine learning techniques. Additionally, to deliver a comprehensive model with better characterization of patients’ data and tumor heterogeneity, we expand our clinical outcome prediction models by including genomic data (represented by RNA sequencing) along with clinical data and radiomics features.