Glioblastomas are the most aggressive and destructive forms of solid brain tumors of the central nervous system. Despite aggressive multimodal treatment approaches, the overall survival period is reported to be less than 15 months after diagnosis. MGMT gene silencing is a potentially proper predictive element in determining the mortality rate for glioblastoma patients undergoing chemotherapy. Analyzing the correlation between different medical image characteristics and MGMT promoter methylation status through machine learning tools could play an essential role in the automatic aided diagnosis approach. By doing data preprocessing and transformation, this thesis aims to extract features from data provided by the Radiological Society of North America (RSNA) and investigate this relationship with various classification methods like Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP).