Accurate segmentation and prognostication of brain tumors are critical for effective diagnosis, treatment planning, and patient management in glioma. In this work, we present a unified framework built upon the BRATS2020 challenge data that integrates deep learning-based segmentation with radiomics and machine learning for overall survival prediction. First, we employ a 3D-UNet architecture to perform robust segmentation of brain tumors from multi-modal MRI scans, achieving a mean Intersection over Union (IOU) of 86%. This segmentation not only delineates tumor sub-regions effectively but also provides the basis for subsequent feature extraction. Leveraging the pre-trained 3D-UNet, we extract deep features from the MRI scans, and in parallel, perform radiomics feature extraction on the corresponding tumor masks. These features are then combined with clinical and demographic data provided in the BRATS2020 challenge dataset. A random forest classifier is subsequently trained on this comprehensive feature set to predict overall patient survival, achieving a classification accuracy of 70% in stratifying patients into survival categories. Our approach builds on recent advances in brain tumor segmentation—incorporating ideas such as ensemble learning, multi-modal imaging, and uncertainty quantification—to enhance both the segmentation accuracy and prognostication performance. The promising results demonstrate that the integration of deep learning segmentation with radiomics and traditional machine learning methods can serve as a robust tool for personalized treatment planning and risk stratification in glioma patients.