Accurate segmentation of the gross tumor volume (GTV) in meningioma is essential for radiation therapy planning. This study evaluates several deep learning-based approaches, including U-Net, Attention U-Net, nnU-Net, and SE-ResUNet (Squeeze-and-Excitation Residual U-Net), for automated GTV segmentation on post-contrast MRI scans. Incorporating squeeze-and-excitation blocks and attention mechanisms improved performance, with SE-ResUNet achieving the highest average Dice score across datasets (80.5%), followed by Attention U-Net (79.3%), nnU-Net (77.6%), and U-Net (73.6%). These results demonstrate that SE-ResUNet provides the most accurate and robust segmentation, while all advanced models outperform the conventional U-Net. Automated segmentation with these methods can reduce clinical workload and variability, offering a reliable tool for planning radiation therapy in meningioma.