Glioblastoma is a common and fatal tumor presenting a poor survival rate. To choose the best course of treatment, patients and providers need to predict the survival rate of patients. Historically, statistical methods have helped analyze clinical features to forecast survival, while recently the same is being accomplished by applying artificial intelligence techniques. However, most of these works are limited to predicting 1-, 2-, or 10-year survivability with several of these works simulating data for balancing the dataset. Hence, there is a need for fine-grained prognosis without tempering the data. To achieve the same, we employ data from Surveillance, Epidemiology, and End Results (SEER) along with an ensemble of classification and regression models to develop a fine-grained model to predict the survival period of glioblastoma patients. The proposed framework titled 'Peshnaja' presents higher resolution in the prognosis of glioblastoma while showcasing an accuracy of 70% with an overall RMSE of 2.65. Moreover, a comparison of Peshnaja with other frameworks shows that we did not impute missing values nor employed synthetic data to force good results, thereby keeping Peshnaja true to the existing data.