Diagnosis of brain tumors from MRI scans is a vital concern in medical imaging that contributes to the need for fast and accurate deep learning models. In this study, it is proposed a Hybrid CNN-ViT Feature Extraction framework that utilizes the local spatial feature extraction capability of Convolutional Neural Networks (CNNs) and long-range dependency capturing ability of Vision Transformers (ViTs). The method starts with a set of advanced preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and data augmentation based on generative adversarial networks (GAN) to help increase image quality and balance the dataset. First, trained by a CNN-based backbone is EfficientNet to obtain low- and mid-level spatial features, the hybrid model is proposed. These feature maps are further converted into patches and input to a Vision Transformer  (ViT) encoder, where self-attention functions to refine global feature representations. The proposed method utilized concatenation and attention-based mechanism for feature fusion, which ensured the discriminative classification of features from both CNN and ViT. Finally, a fully connected layer with the softmax classifier predicts the presence of tumor and its kind. Extensive experiments have been conducted on benchmark brain MRI datasets, which show that the Hybrid CNN-ViT model significantly outperforms traditional CNN-based models and achieves higher accuracy, precision, recall, and F1-score. The study demonstrates the successful application of hybrid deep learning techniques for robust and generalizable brain tumor classification. The novelty of this research lies in integrating spatial information with context attention in enhancing AI-based medical diagnostics.