Accurate Brain tumor segmentation from magnetic resonanceimages (MRIs) is of paramount importance for clinical treatment decisions and surgical planning. Recent advancements have shown promising results indeep convolutional networks for this task. Often rely state-of-the-art models on computationally expensive 3D convolutions and complex ensemble strategies, which pose challenges in terms of computational overhead and system complexity. Additionally, resource constraints necessitate the pursuit of high accuracy within limited computational budgets. In this research, we propose a novel methodology to address the challenges in brain tumor segmentation using the 3D UNet++.This model is a lightweight and efficient pseudo-3D model designed to segment 3D volumetric images in a single pass. 3D UNet++ model builds upon the popular U-Net architecture by incorporating 3D convolutional layers to capture spatial information. It achieves efficient segmentation by performing computations in a single pass, making it suitable for real-time applications. Based on the U-Net architecture, 3D UNet++ enhances the representation capabilities by utilizing dense skip connections and nested U-Net architectures. It efficiently captures spatial information in a hierarchical manner, improving the segmentation accuracy of volumetric images. To evaluate the efficacy of our methodology, we shown extensive experiments on the BraTS 2018 dataset, a widely recognized benchmark for brain tumor segmentation. Performance metrics, such as Dice similarity coefficient (DSC) and sensitivity, were employed to assess the robustness and accuracy of our proposed method.