Maize (Zea mays L.) is a major staple crop that can be affected by a variety of pests and diseases, which can lead to serious consequences for food security and the livelihoods of farmers. Conventional methods of detection are time-consuming, labor intensive, and prone to error and result in delayed diagnosis. In this research we present an automated, deep learning-based system that detects and classifies maize leaf diseases and insect damage using image data. The deep learning models, DenseNet-121 and ResNet-50, were trained collectively based on a unique and publicly available dataset of maize leaf images, collected under realistic field conditions, and labeled by leading maize plant pathologists. After a thorough preprocessing stage, the model was trained and validated using a total of 23 classes of disease and insect issues. Post-training validation results show the comprehensive performance of the models, indicating that DenseNet-121 excelled in accuracy percentages for the majority of classes, particularly for insect-related classes, while ResNet-50 accounted for consistent reliability overall, regardless of some confusion between similar disease classes. Overall, the results indicated the suitability of deploying deep learning to edge or mobile environments in order to assist farmers by continuously monitoring crop health and pathways to decision-making. This approach generates a pathway toward scalable and viable decision-making strategies using intelligent and accessible precision agriculture methods.