Brain tumors pose significant health risks and can vary in severity depending on their type. Accurate classification of brain tumors, such as glioma, pituitary, and meningitis, is crucial for determining appropriate treatment plans. This paper presents a classification approach for brain tumors in MRI scans. The CNN model employs convolutional layers for feature extraction, batch normalization to improve training stability, and dropout regularization to reduce overfitting. These architectural choices enable the model to perform efficiently on test data to provide reliable predictions. Trained on a diverse dataset comprising 5,848 brain MRI samples across three sources, the model effectively classifies the four categories. Among several tested architectures, ResNet18 demonstrated superior performance, achieving the highest testing accuracy of 99.69% and an average accuracy of 98.44%. Data augmentation and transfer learning further enhanced the model’s performance. Evaluation metrics highlight its robustness and potential for clinical use. This automated classification approach aids radiologists in the diagnosis process with improved outcomes.