<p>Brain cancer involves the presence of abnormal cells in the brain or nearby areas, which annually results in high mortality rates worldwide. Magnetic Resonance Imaging (MRI) helps in segmenting brain tumors, dividing them into different parts; however, accurately segmenting these areas is challenging due to variations in tumor location, size, shape, and intensity. To address this challenge, CNN architectures, based on U-Net, are specifically designed for image segmentation tasks. Although U-net is recognized primarily as a segmentation tool, it has also been applied in various other applications. This survey presents various U-Net architectures, highlighting the expanding potential of U-net beyond segmentation tasks. In addition to exploring convolutional neural networks based on the U-Net architecture, we also investigate segmentation, detection, classification, and the challenges associated with each of these tasks. This survey also includes discussion of the state-of-the-art in performance evaluation metrics for models. In contrast to earlier surveys that focus only on segmentation, the current survey takes a broader view. Specifically, the unique contribution of this survey is a cross-task analysis that integrates and compares U-Net approaches for brain tumor segmentation, detection, and classification using MRI images. We highlight how U-Net variants have evolved over time, emphasizing their architectural innovations and versatility across different tasks. In addition, the current survey identifies and describes research gaps and offers practical directions for future work, such as exploring multimodal data integration, domain adaptation, and explainable AI techniques.</p>