Accurate glioma segmentation in 3D MRI scans is crucial for diagnosis and treatment planning. However, supervised deep learning models are limited by the need for expert annotations. In this study, we introduce an unsupervised two-step approach for multi-class glioma segmentation eliminating the dependency on labeled data. First, a robust global tumor mask is extracted using adaptive thresholding (Sauvola) to dynamically adjust intensity variations, followed by morphological processing for noise removal and refinement. A strict 3D fusion across axial, coronal, and sagittal planes ensures spatial consistency, reducing segmentation artifacts. Second, an optimized hierarchical unsupervised clustering method (HUP-OAP) is applied to classify tumor regions into active tumor, edema, and necrosis based on multi-modal MRI intensity distributions. This method leverages affinity propagation based clustering, ensuring anatomical coherence and precise tumor subregion segmentation. Our method is evaluated on the BraTS 2021 dataset, achieving an average improvement of 13% in Dice score (DSC) and a reduction of 3.58 in Hausdorff Distance (HD), demonstrating competitive performance compared to supervised deep learning methods without requiring manual annotations. The improved segmentation accuracy and reduced boundary errors have direct implication for clinical applications, including more precise surgical planning, minimizing damage to healthy tissue, enhanced radiotherapy targeting, ensuring optimal dose delivery, and improved treatment monitoring, aiding in better tumor progression tracking. Our results suggest that this approach offers a scalable, interpretable, and annotation-free alternative for automated glioma segmentation, making it highly suitable for clinical applications.