Research on glioblastoma has led to the development of various approaches using radiomics and MGMT promoter methylation to improve prognostication for newly diagnosed patients. These approaches involve the use of machine learning, deep learning, and other emerging technologies to analyze MRI data and predict patient outcomes. Studies have explored the potential applications of these methods, including their use in resource-limited settings and their ability to detect residual tumors and predict prognosis.