Early identification of brain tumors is essential for saving and extending the patient’s life. Brain tumors can be devastating if they are not detected in time. The ability and knowledge of the radiologist, which may only sometimes be available, are required for making a manual diagnosis of brain tumors. Manual procedures are also time-consuming, inaccurate, and ineffective. Therefore, a workable solution is required to ensure a precise diagnosis. Procedure evolution and in-depth research over time have established that many Machine Learning (ML) indicative systems must be capable of identifying and analyzing brain tumors. Many segmentation algorithms have been developed to accomplish this and increase the classification accuracy of brain tumors. Professionals in medical image processing have recognized the intricacy and challenge of segmenting brain images. A novel automatic technique for detection and categorization is suggested in this work. An automated magnetic resonance imaging (MRI) method to detect brain tumors is discussed in this article. The experiments’ findings showed that the suggested model had a good accuracy of 98.53%. The results of the inquiry into the recommended model’s efficacy using quality metric measures, such as precision, recall, f1 score, and support.