The necessity of studying early brain tumor diagnosis has increased significantly. The patient’s survival rate is increased when the tumor is discovered early for initial treatment. Due to the significant processing overhead caused by the enormous volume of image input to the processing system, processing magnetic resonance imaging (MRI) for early tumor diagnosis is difficult. As a result, there was a major delay, and the system’s efficacy decreased. This has led to a surge in the need for a better detection system for accurate segmentation and precise representation for processing that is both faster and more accurate. The most recent research has proposed developing novel techniques for brain tumor detection that rely on improved learning and processing. This paper offers a brief summary of the developments linked to MRI. The ability of machine learning (ML) algorithms to learn and process finely has improved the efficiency and accuracy of processing for brain tumor identification in automated systems that are currently in place. Current advancements in automation linked to brain tumor identification are examined, as well as the limitations, benefits, and future prospects of current systems for computer-aided diagnostics (CAD) in brain tumor detection. In the study that is being presented, researchers examine the development of several techniques that have been proposed to imaging brain tumors in a range of fields.