Identifying and segmenting brain tumors using multi-sequence 3D volumetric MRI scans is time-consuming and challenging. Deep learning-based automatic image segmentation approaches are promising solutions to segment brain tumors from MRI 3D reconstructed images. However, T1, T1c, T2, and FLAIR modalities, along with High Graded Gliomas (HGG) and Low Graded Gliomas (LGG), make automatic brain tumor segmentation using deep learning a challenging task. A novel Nested Deep Neural Network (NDNN) has been designed, implemented, and experimented with in this paper, along with an innovative Multimodality Fusion Network (MFS Net). The proposed network segments brain tumors from 3D volumetric images and imposes the extracted feature map on the 3D region with 90.02%, 85.11%, and 85.41% dice score for Whole Tumor (WT), Core Tumor (CT), and Enhancing Tumor (ET) respectively. The novel architecture, innovative multimodality fusion, and outstanding performance of the proposed methodology have been studied, demonstrated, and compared in this paper.