Brain tumors can arise as a result of the rapid and uncontrolled multiplication of the cells. Without timely intervention, this can pose significant dangers. Cells proliferate rapidly and uncontrollably, potentially leading to the formation of brain tumors. Despite numerous notable attempts and promising outcomes, reliable segmentation and classification in this domain remain challenging tasks. The variability in tumor’s physical features and its whereabouts further complicates identification of brain tumors. The survey aims to provide analysts with an extensive literature review on the identification of brain tumors using MRI. The review includes topics such as quantum machine learning (QML), transfer learning (TL), brain tumor anatomy (BMT), deep learning (DL), publicly available datasets (PUD), augmentation techniques (AUG), classification, segmentation, and feature extraction. The study addresses relevant research on brain tumor detection, detailing its merits, limitations, recent advancements, and potential future directions.