Bibi, KhadijaKhadijaBibiDr. NAWAZ MehmoodKhan, SheheryarSheheryarKhanDaud, MuhammadMuhammadDaudMasood, AnumAnumMasoodAbdelgawad, Muhammad AshrafMuhammad AshrafAbdelgawadAbbasi, Syed Muhammad TariqSyed Muhammad TariqAbbasiKhan, AhsanAhsanKhanYuan, WuWuYuanRizwan2025-09-272025-09-272025NMR in Biomedicine, 2025, vol.38(11), article no. e70141.0952-34801099-1492http://hdl.handle.net/20.500.11861/25778<jats:title>ABSTRACT</jats:title><jats:p>Manually segmenting brain tumors in magnetic resonance imaging (MRI) is a time‐consuming task that requires years of professional experience and clinical expertise. To address this challenge, researchers have proposed artificial intelligence–based strategies that enable quick and automatic segmentation of brain tumors. These AI techniques are crucial for the early identification of brain tumors, leading to earlier diagnoses and significant therapeutic benefits. convolutional neural networks (CNN), vision transformers (ViT), and other automated approaches that leverage machine learning and deep learning techniques have demonstrated effectiveness in diagnosing tumor type, size, and location. Consequently, brain tumor segmentation has emerged as a prominent issue in medical image analysis. This study aims to provide a concise review of MRI techniques and examine popular approaches for segmenting brain tumors. It highlights notable advancements in this field over the past several years. To ensure comprehensive coverage of technical topics, including network architecture design, segmentation in unbalanced settings, and multi‐modality processes, over 200 scholarly publications have been meticulously selected for discussion. Based on this literature review, CNN‐based methods and hybrid approaches have shown exceptional results in segmenting brain tumors from MRI images. Additionally, our study outlines the challenges and potential avenues for future research in brain tumor segmentation techniques.</jats:p>enBrain Tmor SegmentationComputed TomographyConvolution Neural NetworksDeep LearningFoundation ModelsMachine LearningMagnetic Resonance ImagingTransformersPeer Reviewed Journal Article10.1002/nbm.70141