This project utilizes a Convolutional Neural Network (CNN) to detect brain tumors and identify their types from MRI images. By automating this critical diagnostic process, the model aims to assist doctors, reducing the time and cost associated with manual tumor detection. The model was trained on a publicly available dataset containing over 2000 MRI images. It achieved a high accuracy of 97% on training data and 93% on validation data, making it a reliable tool for aiding in medical diagnoses. The CNN architecture includes convolutional layers that extract important features from the images, and a dense layer that helps classify the tumor type. Results are visualized using a confusion matrix, and the model is user-friendly, requiring only an MRI image to generate predictions. While the model shows great promise, further improvements could be made by expanding the dataset and adding more labels to detect additional brain conditions. To learn more, please visit the GitHub link.