Lung cancer remains one of the leading causes of cancer-related deaths globally, where early detection plays a crucial role in improving survival rates. The disease includes various types, such as adenocarcinoma, large cell carcinoma, and squamous cell carcinoma, which can be identified using lung CT imaging. However, accurately classifying these subtypes through imaging is challenging due to their similar visual features. Recent progress in artificial intelligence, particularly deep learning, has introduced effective methods to enhance diagnostic precision through automated image analysis. This research proposes a hybrid deep learning approach that combines ResNet50 with a Vision Transformer (ViT) for classifying lung cancer CT images. ResNet50 effectively captures hierarchical spatial features, while the ViT component leverages global attention to extract contextual information. The proposed model was trained for 100 epochs, achieving 99% training accuracy, 97% validation accuracy, and an F1-score of 97.6%. Additionally, the training loss decreased to 0.09, and the validation loss was reduced to 0.015, indicating stable learning with minimal overfitting. Compared to standalone models like OP-CNN, VGG-19, and ResNet50, the hybrid model consistently achieved higher performance, with precision of 97.8%, recall of 97.4%, and better overall reliability. This study highlights the effectiveness of combining convolutional architectures with transformer-based models for lung cancer detection and classification in medical imaging.