Brain tumors rank as the 10th leading cause of death for men and women, affecting both adults and children1 . This underscores the urgent need for effective strategies in prevention, diagnosis, and treatment to address this significant health challenge. Magnetic resonance imaging (MRI) is the preferred method for identifying brain tumors. Recent advancements in image classification technology, particularly Convolutional Neural Networks (CNNs), have greatly improved tumor classification accuracy. The findings of this study have significant implications for clinicians specializing in the early detection of brain tumors. By leveraging advanced neural network models, healthcare professionals can potentially improve the accuracy and efficiency of tumor diagnosis, leading to better patient outcomes and possibly earlier interventions. This underscores the importance of leveraging cutting-edge technology, such as deep learning and neuroimaging, in medical diagnostics. In this study, CNNs are used for brain tumor classification, successfully categorizing brain images into two classes: benign and malignant with an impressive accuracy of 97%.