Brain tumors occur when cells in the brain grow and multiply too quickly. Detecting these tumors early is very important because it can save many lives. Brain tumors can be grouped into different types based on where they start, how fast they grow, and how far they have developed. Correctly identifying the type of tumor is necessary for choosing the best treatment.Braintumor segmentation means marking the exact area where the tumor is located in brain images. Doctors can do this manually, but it requires a lot of expertise, and checking many images can be very slow and tiring. Because of this, automatic methods for identifying and separating tumor regions are needed to make diagnosis faster and more accurate. Brain imaging methods such as CT scans and MRI scans help detect tumors safely and quickly. Today, machine learning (ML) and artificial intelligence (AI) techniques are being used to automatically classify and segment brain tumors from these images. The quality of the segmentation method greatly affects how accurately tumors can be identified, which directly influences diagnosis and treatment decisions. This review explains different types of brain tumors, available public datasets, image enhancement techniques, segmentation methods, feature extraction processes, and classification approaches. It also covers machine learning methods, deep learning models, and transfer learning techniques used for brain tumor detection. The goal of this study is to combine brain imaging technologies with computer-based methods to improve brain tumor analysis. It also highlights current challenges in these engineering methods and suggests future directions for better diagnosis systems.