Premature recognition of brain tumors is crucial. Only biopsies can classify brain cancers, requiring invasive brain surgery. Brain malignancies can be identified and classified by medical professionals with the use of computationally focused procedures. Here, we present a deep learning approach based on a comparison of several recent studies that used a variety of machine learning techniques to diagnose three different tumor types using magnetic resonance brain images: glioma, meningioma, and pituitary gland in addition, no tumors were also included in the analysis. This makes it possible for doctors to accurately diagnose malignancies in their early stages. In this study, 7023 MRI brain pictures were utilized in this investigation. The Xception network creates a novel convolution neural network (CNN) once the images have been preprocessed and enhanced. Multiple convolution layers with 3 * 3 kernel functions are present in the network. To avoid overfitting, batch normalization layers were utilized, and the ReLU function served as each layer’s activation function. The Adamax optimization function was used with a 0.001 rate of learning to maximize efficiency. The suggested model’s validation accuracy was determined to be 99.70%, while its training accuracy was found to be 99.98%. The current investigation demonstrates that the suggested CNN Xception Net model has achieved the best classification accuracy for brain tumors. This model obtained excellent performance and optimal execution time compared with other CNN and machine learning approaches used in earlier research. Radiologists and doctors can utilize this recommended network in healthcare facilities to diagnose brain tumors.