Corn, a vital agricultural crop, and essential food source, plays a crucial role in the global food chain and serves as a raw material for various industrial applications like biofuels. Small-scale corn cultivation sustains livelihoods in developing nations, but these crops are highly susceptible to diseases. Extreme weather conditions can exacerbate these diseases, leading to significant declines in agricultural yields. Advancements in artificial intelligence (AI), particularly deep learning algorithms, offer promising solutions. This study explores the application of deep transfer learning for classifying three distinct corn leaf conditions: rust, northern leaf blight, and healthy plants. By utilizing corn leaf images as input and leveraging convolutional neural networks, the proposed approach eliminates the need for complex pre-processing or manual feature extraction. Employing well-established deep learning models (VGG19, GoogleNet, and ResNet50) and rigorous evaluation methods with various data splitting scenarios, the study achieved remarkable mean accuracies of 96%, 99%, and 75% in distinguishing the three classes. These results demonstrate the potential for developing practical applications to assist farmers and plant pathologists in accurately and swiftly identifying corn diseases, enabling them to implement appropriate treatment measures.