This study presents an innovative approach to brain tumor classification using the Spatial Pyramid Pooling Convolutional Neural Network (SPP_CNN). The classification of brain tumors from medical images is a critical task in medical diagnostics, demanding high accuracy and robustness. Traditional Convolutional Neural Networks (CNNs) often struggle with variations in input image sizes, significantly affecting their performance. The proposed SPP_CNN model addresses this challenge by integrating a Spatial Pyramid Pooling (SPP) layer, enabling the network to generate fixed-length representations regardless of the input size. This layer enhances the model’s ability to learn and generalize from the spatial hierarchies present in the image data. Comprehensive experiments on a standard brain tumor dataset were conducted, comparing the performance of SPP_CNN with other advanced CNN architectures, including GCB_CNN and R_CNN. The results demonstrate that SPP_CNN outperforms these models in terms of accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). Specifically, SPP_CNN achieved an accuracy of 97%, with significant improvements in handling variations in tumor size and shape. The inclusion of the SPP layer not only enhances the model’s robustness to spatial transformations but also improves its feature extraction capabilities. This study underscores the potential of SPP_CNN as a powerful tool for brain tumor classification, offering a significant advancement over traditional CNN approaches. Future work will explore the application of SPP_CNN to other medical imaging tasks and the integration of additional advanced techniques to further enhance its performance.