Brain tumor segmentation from MRIs has been a continuing challenge for radiology and neurology specialists. Therefore, a reliable method that captures the growing and refined data in this regard, is much needed. Among many deep learning techniques having been proposed for medical image analysis, U-Net-based variants are found to the most used models in multimodal medical image segmentation. Moreover, knowing that brain tumors can have various shapes, sizes, and appearances, the segmentation task, using simple block architectures, does not capture all the intricacies of the tumor boundaries and internal structures. Therefore, more complex architectures, such as U-Net and its 3D extensions, are better suited to handle these variations and provide better segmentation results. In this paper, we introduce an enhanced automated 3D brain tumor segmentation network built on the foundation of the 3D U-Net architecture. Through the combination of the advantages of UNet, ResNet, we created a new 3D UNet model with block modifications, based on simple ResNet block, BNet’s, with batch normalization. We used the 2020 Multi-modal Brain Tumor Segmentation Challenge (BraTS2020) data sets to train and validate the proposed model. The BraTS2020 validation data set yielded dice values of 0.86, 0.82 and 0.85 for the Whole Tumor (WT), for Enhancing Tumor core (ET), and for Tumor Core (TC), respectively. The experimental results show that our model significantly outperforms the standard brain tumor segmentation methods. © 2023 IEEE.