A brain tumor is a type of cancer that can be life-threatening or significantly impair quality of life. Deep learning techniques enable more efficient identification and treatment of tumors. Brain MRI images are utilized in various ways to detect malignancies, with deep learning methods outperforming others.Brain tumor detection and classification from MRI images is a critical task in medical diagnostics, requiring high accuracy and reliability. This research paper proposes a comprehensive system leveraging advanced deep learning techniques to enhance brain tumor classification. The dataset used consists of approximately 5000 MRI images categorized into three tumor typesGlioma, Meningioma, and Pituitaryand a healthy class depicting no tumor. The proposed system employs a custom Convolutional Neural Network (CNN) along with four prominent transfer learning models: MobileNet, ResNet-152, VGG-16, and DenseNet-169. The preprocessing steps include resizing, normalization, and augmentation to improve training efficiency and model performance. Each model's accuracy and robustness were evaluated, with MobileNet achieving the highest accuracy of 98.63%, followed by VGG-16 at 98.62%, the custom CNN at 98.32%, ResNet-152 at 97.71%, and DenseNet-169 at 96.56%. The ensemble classifier approach combines these models to leverage their strengths, resulting in improved prediction reliability and accuracy. This research provides a significant contribution to the field of medical image analysis, offering a robust and accurate tool for aiding in the early detection and classification of brain tumors.