Radiomics is the process of extracting valuable features from medical images, which can be further analyzed to improve diagnosis, prognosis, and clinical decision support in oncology, ultimately leading to the advancement of precision medicine. Radiomics is based on the extraction and modeling of imaging features for analysis, serving as an extension of computer-aided diagnosis (CAD) applications. The emergence of Artificial Intelligence (AI) has pushed radiomics by providing new insights and significantly advancing its implementation in clinical practice. Therefore, this paper aims to analyze and demonstrate the clinical aspects of AI-based radiomics, with a specific focus on glioblastoma (GBM), a malignant brain tumor. Furthermore, this paper highlights the current state of radiomics research pertaining to GBM, with potential future challenges and issues in the field.