This article presents a hybrid deep convolutional neural network (CNN) model designed to enhance early detection and classification of brain tumors. The model is trained on publicly available datasets and optimized using grid search to automatically tune hyperparameters. Three distinct CNN models are developed for different classification tasks: detecting brain tumors with 99.53% accuracy, classifying brain tumors into five types (normal, glioma, meningioma, pituitary, and metastatic) with 93.81% accuracy, and grading glioma brain tumors into three grades (II, III, IV) with 98.56% accuracy. The models outperform classical CNNs like AlexNet, DenseNet121, ResNet-101, VGG-19, and GoogleNet in terms of accuracy, sensitivity, specificity, and F1-score. The study highlights the superior performance of the proposed deep learning approach in advancing brain tumor classification and early detection, emphasizing the importance of automated and accurate methods in medical imaging.This article presents a hybrid deep convolutional neural network (CNN) model designed to enhance early detection and classification of brain tumors. The model is trained on publicly available datasets and optimized using grid search to automatically tune hyperparameters. Three distinct CNN models are developed for different classification tasks: detecting brain tumors with 99.53% accuracy, classifying brain tumors into five types (normal, glioma, meningioma, pituitary, and metastatic) with 93.81% accuracy, and grading glioma brain tumors into three grades (II, III, IV) with 98.56% accuracy. The models outperform classical CNNs like AlexNet, DenseNet121, ResNet-101, VGG-19, and GoogleNet in terms of accuracy, sensitivity, specificity, and F1-score. The study highlights the superior performance of the proposed deep learning approach in advancing brain tumor classification and early detection, emphasizing the importance of automated and accurate methods in medical imaging.