One of the biggest health problems around the world, display clearly by brain tumors, is finding issues early. Making the right choice is very important for treatment. MRI scans are particularly good at helping numeral out the shape and building of brain tumors. In any case, traditional diagnostic schemes relying on manual interpretation take time and are vulnerable to errors of meticulous transient origin. This paper explores the recent advances of Artificial Intelligence (AI) and Machine Learning (ML) to automate brain tumor detection and prediction in MR images. It stresses deep learning patterns in particular, convolutional neural networks (CNNs) and Xception, that exactness is. From end to end, there should be no traces left. These points out certain steps prior to analysis, such as extracting a cranium or normalising data. They also illustrate different kinds of information addition into the learning process to cope with over-predictions and more example instances. It also discusses performance evaluation measures and problems, such as the computational cost associated with mass data processing and virulent bias of example sets from certain regions. Address Considering these restrictions, the AI-based systems suggested building a bridge between algorithm development and its practical application. Thus, we can improve the diagnostic reliability, efficiency and better outcomes for neuro-oncology patients.
Singh et al. (2025) studied this question.