A human body is a system of many organs and structures and the most important and sensitive is the brain. Now, one of the well-known causes of brain dysfunction is a tumor that is classified as the uncontrolled growth of excessive cells. These cells consume nutrients that would otherwise go to healthy cells resulting in brain failure. In the past, doctors must literally look at medical resonance images to identify the exact locations of tumors in the brain as well as their size which is time-consuming and is also very subjective in terms of the data acquired. Central nervous system cancer or more specifically brain cancer is an occupying disease that has led to the loss of numerous lives and due to this its diagnosis at the early stage of development is crucial. This review is centered on the Convolutional Neural Network (CNN)-based image segmentation for the detection of brain tumors. They use different techniques of image processing such as image segmentation, image enhancement, and feature extraction to enhance the tumor detection in MRI images better and faster. The detection process involves four key stages: It mainly includes image preprocessing, image segmentation, feature extraction, and classification. It was noted that demonstrating the effectiveness of image processing and neural networks in identifying and classifying brain tumors in MRI scans.