Magnetic resonance imaging (MRI) is widely utilized for brain tumor segmentation, yet significant challenges persist due to intensity variations, irregular boundaries, and substantial morphological heterogeneity. Current state-of-the-art deep learning methods often struggle to capture long-range spatial dependencies, delineate fine boundary details, and efficiently process 3D volumetric data. This study introduces a novel hybrid framework that integrates state-space models with frequency-domain learning to address these limitations. The proposed model offers four primary contributions: (1) incorporation of a morphological attention block in the encoder to enhance boundary localization via dilation-erosion gradient modeling; (2) a dual-domain bottleneck module that combines Mamba-inspired sequential modeling with the Fourier Neural Operator (FNO) for efficient local and global pattern modeling with linear complexity; (3) a Feature Pyramid Network (FPN) augmented with Feature-Guided Learning (FGL) for adaptive multi-scale semantic fusion; and (4) Laplacian Pyramid decomposition to preserve high-frequency edge details. The model demonstrates state-of-the-art performance, achieving Dice Similarity Coefficients of 0.81 ± 0.05, 0.92 ± 0.02, and 0.86 ± 0.03 for Enhancing Tumor (ET), Whole Tumor (WT), and Tumor Core (TC) on the BraTS2020 dataset, respectively, with a mean Dice of 0.86 ± 0.05. MambaFNO-NET attains Hausdorff distances (HD95) of 4.21, 6.22, and 6.85 mm for ET, WT, and TC, respectively, resulting in a mean HD95 of 5.76 mm, which underscores its superior boundary localization accuracy. Overall, MambaFNO-NET delivers an efficient and accurate solution for 3D brain tumor segmentation, balancing volumetric precision with spatial boundary alignment for practical clinical deployment.