Keratoconus is a progressive eye disorder that, if undetected, can lead to severe visual impairment or blindness, necessitating early and accurate diagnosis. The primary objective of this research is to develop a feature fusion hybrid deep learning framework that integrates pretrained Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) for the automated classification of keratoconus into three distinct categories: Keratoconus, Normal, and Suspect. The dataset employed in this study is sourced from a widely recognized and publicly available online repository. Prior to model development, comprehensive preprocessing techniques were applied, including the removal of low-quality samples, image resizing, rescaling, and data augmentation. The dataset was subsequently partitioned into training, testing, and validation subsets to facilitate robust model training and performance evaluation. Eight state-of-the-art CNN architectures, including DenseNet121, EfficientNetB0, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16, and VGG19, were utilized for feature extraction, while the ViT served as the classification head, leveraging its global attention mechanism for enhanced contextual learning, achieving near-perfect accuracy (e.g., DenseNet121+ViT: 99.28%). This study underscores the potential of hybrid CNN-ViT architectures to revolutionize keratoconus diagnosis, offering scalable, accurate, and efficient solutions to overcome limitations of traditional diagnostic methods while paving the way for broader applications in medical imaging