This paper proposes a new method for the classification of brain tumor types on MRI scans with firefly optimization along with parallel CNN. As accurate diagnosis of the tumor type is greatly needed in managing patients, our methodology improves upon the basic CNNs by integrating firefly optimization for feature selection and parameter tuning. In order to test our approach, we applied it to a large-scale dataset of MRI scans and illustrated notable improvements in classification accuracy as well as computational efficiency over the existing methods. Our results suggest that such a framework not only speeds up the process of classification but also enhances robustness in predictions, thus giving clinicians a quality tool for the early detection and treatment planning of brain tumors.