Histopathological examination of biopsy samples is still used in the diagnosis and classifications of brain tumors today. To increase the precision of tumor detection, an efficient automated system must be put in place. The existing procedure is intrusive, time-consuming, and prone to human mistake. In order to improve the classification accuracy of brain tumors, this paper uses convolutional neural networks (CNN) to detect many classifications of brain tumors for early diagnosis reasons. Images from a real-time dataset were taken, showing different tumor sizes, locations, shapes, and image intensities. This paper will also go into detail about the procedures on CNN and SVM to classify the brain tumor images. The classification is done by using precision, support and F1-score during the planning and research phases as well as the design, development, and testing phases of this application, as well as the requirements, methodologies, and current systems associated to it. 18 people with relevant medical backgrounds participated in testing sessions, and the great majority of participants resoundingly agreed that the system application successfully classifies brain tumors from MRI scan pictures and achieves high accuracy of hybrid CNN and SVM. Future research should focus further on other elements that can identify and categorize brain tumors, since this web app’s brain tumor classification system is intended to be improved.