Glioma is a type of malignant brain tumors which requires early detection for patients Overall Survival (OS) prediction and better treatment planning. This task can be simplified by computer aided automatic segmentation of brain MRI volumes into sub-regions. The MRI volumes segmentation can be achieved by deep learning methods but due to highly imbalance data, it becomes very challenging. In this article, we propose deep learning based solutions for Glioma segmentation and patient’s OS. To segment each pixel, we have designed a simplified version of 2D U-Net which is slice based and to predict OS, we have analyzed radiomic features. The training dataset of BraTS 2019 challenge is partitioned into train and test set and our primary results on test set are promising as dice score of (whole tumor 0.84, core tumor 0.80 and enhancing tumor 0.63) in glioma segmentation. Radiomic features based on intensity and shape are extracted from the MRI volumes and segmented tumor for OS prediction task. We further eliminate the low variance features using Recursive Features Elimination (RFE). The Random Forest Regression is used to predict OS time. By using intensities of peritumoral edema-label 2 of Flair, the necrotic and non-enhancing tumor core-label 1 along with enhancing tumor-label 4 of T1 contrast enhanced volumes and patients age, we are capable to predict patient’s OS with considerable accuracy of 31%.