Maize is a cornerstone of food security and economic stability in Nigeria, yet its production is severely hampered by crop diseases that cause significant yield losses and threaten the livelihoods of millions of smallholder farmers. Despite advances in machine learning (ML) and deep learning (DL) for plant disease detection, existing solutions often lack generalizability, scalability, and accessibility for resource-limited settings. This research used a robust, predictive system that leverages convolutional neural networks, specifically ResNet50 and EfficientNet trained on diverse, annotated datasets of maize leaf images. By integrating computer vision, transfer learning, and user-centric mobile application design, the system aimed to provide real-time, accurate diagnosis and actionable recommendations for disease management. This study compared the performance of the ResNet50 and the EfficientNet. At the end of the research, ResNet50 achieved marginally higher accuracy than EfficientNet under the same experimental conditions, although the performance difference is small and not statistically tested. The ResNet50 model was thereafter deployed into a scalable mobile application tool that can empower farmers and extension workers with early disease detection capabilities, potentially reducing crop losses, improving productivity, and enhancing food security across sub-Saharan Africa.