Wheat leaf diseases pose a significant threat to global food security by reducing crop yield and quality, directly impacting the over 35% of the global population that relies on wheat as a dietary staple. While recent advances in deep learning have enabled automated disease diagnosis from leaf images, the choice of model architecture remains pivotal for achieving both diagnostic accuracy and practical deployment efficiency. This study presents a comprehensive comparison between traditional Convolutional Neural Networks (CNNs) and modern Transformer-based models for wheat disease detection. Experiments were conducted on the publicly available “Wheat Disease Dataset (Small)” from Kaggle, comprising 1,455 labeled wheat leaf images categorized into five key classes: Brown Rust (294 images), Yellow Rust (277), Mildew (351), Septoria (327), and Healthy (206). These images, captured under diverse real-world agricultural conditions—varying lighting, orientations, backgrounds, and disease severity stages—were pre-processed via resizing to 224 × 224 pixels, pixel normalization, and extensive data augmentation to reflect field variability. Performance evaluation using accuracy, precision, recall, and F1-score metrics demonstrated that the Swin Transformer achieved the highest classification accuracy of 95%, outperforming both CNN-based and other Transformer architectures. Beyond achieving strong empirical results, this research highlights the tangible real-world applicability of Transformer-based models for precision agriculture. The findings support deployment in mobile applications for farmers, UAV-based crop surveillance systems, and digital early-warning advisory platforms. By enabling rapid, accurate, and accessible in-field disease detection, this work contributes to reducing crop losses, optimizing fungicide application, and strengthening global food security through scalable, AI-powered agricultural diagnostics.