Early MRI detection of a brain tumor is a critical step in prioritizing patient care and treatment. Due to the nature of MRI imaging, it is often difficult to identify the disease area by eye, and the preferences may vary among different doctors. As a result, accurate and speedy diagnosis is critical. However, accurate classification of MRI images for brain tumor detection plays a vital role in an effective diagnostic and treatment planning. We present an efficient AI framework based on a strong feature extraction component employing a convolutional neural network (CNN) and powerful machine learning classifiers such as decision tree (D-Tree), linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and K-nearest neighbors (KNN) for tumor classification. The proposed method is validated using the BRATS 2023 brain MRI database, and performance evaluation results showed that CNN+ SVM had the peak classification accuracy; thus, it is a good fit for high-accuracy offline diagnostics, while CNN+ decision tree had the lowest inference time; therefore, this is more suitable for real-time applications. CNN+ LDA and CNN+ logistic regression were well balanced between accuracy and computational efficiency, with the only compromise seen with CNN+KNN given its slow inference time. The most salient aspect of this work was the integration of an App Designer MATLAB tool for visualization. To enhance usability and visualization, an interactive application is developed using App Designer that allows medical practitioners to easily interpret classification outputs.