Maize is a crucial crop globally, playing a significant role in economic systems. Timely detection of diseases in maize leaves is essential to ensure high crop yields. However, traditional manual inspection methods are slow and often inaccurate, highlighting the need for automated approaches. The integration of machine learning and IoT in smart farming offers promising solutions for early disease detection and prevention, which are key to sustainable agriculture. Deep learning has been increasingly utilized in identifying plant leaf diseases, with many systems employing vision-based machine learning for real-time detection. Convolutional Neural Networks (CNNs) have delivered impressive results in this area. However, the newer concept of Vision Transformers (ViTs) in vision-based deep learning is gaining attention. Although ViTs have shown potential in image classification, their application in plant leaf disease classification is still underexplored. This paper introduces MaizeViT, a hybrid model that combines Vision Transformer and CNN specifically for maize leaf disease classification. MaizeViT’s performance is evaluated using publicly available datasets, and it outperforms standard CNN models such as VGG16, DenseNet121, ResNet50, MobileNetV2, and InceptionV3. The results demonstrate that MaizeViT achieves an average accuracy of 98.15% and a precision of 98.31%.