Brain tumor detection is crucial for early diagnosis, effective treatment, and improved patient outcomes. This paper presents an advanced approach to brain tumor classification utilizing MRI imaging and deep learning techniques, specifically customizing the Xception model. The research emphasizes binary classification models, evaluating their efficacy through ROC curves and AUC analysis. Utilizing publicly available MRI datasets, our model architecture leverages pre-trained CNNs and transfer learning, with a focus on fine-tuning the Xception model for optimal performance. We explore the impact of our brain tumor detection system on diagnostic accuracy and patient outcomes in neuroimaging, underscoring the potential clinical implications. Moreover, we highlight the necessity for further research and validation studies to validate the model’s effectiveness across diverse patient cohorts and clinical scenarios. Notably, our proposed model achieved an impressive 99.88% classification accuracy.