Maize (Zea mays L.) production in Uganda is threatened by devastating diseases including Maize Streak Virus (MSV), Grey Leaf Spot (GLS), and Common Rust (CR), which collectively reduce yields by 30–80% in affected fields. This study developed, tested, and evaluated an AI-based mobile application for early detection and classification of these three major maize diseases in Rukungiri District, Uganda. The application employs a Convolutional Neural Network (CNN) architecture trained on a dataset of 8,420 maize leaf images to classify diseases with high accuracy. The final model achieved an overall accuracy of 94.3%, with precision, recall, and F1-scores exceeding 0.91 for all three disease classes. Field testing with 60 smallholder farmers in Rukungiri demonstrated that the application improved disease identification speed and accuracy compared to traditional visual scouting. Farmers expressed high satisfaction with the app's usability and relevance. The study concludes that AI-powered mobile tools hold significant promise for precision agriculture and disease management in resource-limited settings.