In order to identify brain tumors based on Magnetic Resonance Imaging (MRI), the study discusses a brief review of a few common Machine Learning (ML) and Deep Learning (DL) algorithms that have been explored in research papers. The beginning of the article discusses some of the most popular conventional approaches, then followed by discussion of ML algorithms for MR image segmentation, such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Fuzzy C-Means (FCM), ‘K-Nearest Neighbour (KNN)’ etc., in addition, a brief overview of some of the most popular DL techniques, such as Long Short-Term Memory Networks (LSTMN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks, etc., which are used by researchers are also discussed. Techniques using ML and DL have been shown to be quite effective over conventional methods, offers several advantages such as time-saving, cost-effectiveness and a wide variety of applications in various fields. In some of literatures conventional segmentation, ML and DL based segmentation are implemented together to utilize advantages of all these techniques. The study provides a reader with an understanding of the fundamental processes in image analysis, the most widely utilized approaches in conventional, ML, and DL for the segmentation, extraction of features, and classification of tumors identified by MRI brain images. As well as standard datasets and performance measures are often utilized by researchers. The paper finally concluded with possible improvements for future research and unresolved issues with brain tumor segmentation.