Accurate diagnosis of brain tumors is crucial for providing patients with the best care and treatment options. Many glial cell neoplasms, or gliomas, call the brain, and spinal cord their home. Patients benefit from better treatment planning and faster, more accurate glioma diagnoses. The development of automated categorization systems to aid radiologists in reducing diagnostic errors has shown encouraging results, thanks to advancements in deep learning. Due to their performance, convolutional neural networks (CNNs) find extensive usage in medical imaging. As part of this project, we will introduce GliomaAI, a model for classifying brain tumors using MRI data, and test out various deep learning architectures. The goal is to demonstrate the model’s superiority by comparing CNN classification accuracy. Using F1-score, precision, recall, and accuracy, we evaluated the classification accuracy of the models. Based on our findings, current models such as EfficientNet and VGG16 demonstrate impressive accuracy. The precision with which our suggested GliomaAI model surpasses these outcomes is quite remarkable. It appears that GliomaAI can significantly improve the accuracy of brain tumor classifications, based on its exceptional performance. The accuracy of GliomaAI allows radiologists to make reliable neuro-oncology diagnoses. Clinical applications will be the primary emphasis of future research into GliomaAIin order to determine its effectiveness and potential for widespread use in healthcare. Advanced convolutional neural network (CNN) structures have the potential to revolutionize medical diagnoses and enhance patient care, as shown in this study.