This study explores the application of the MobileNetV2 architecture for detecting cataracts and pterygium using anterior segment photographed images (ASPI) captured via smartphone cameras. Cataracts and pterygium are significant global health concerns, and their early detection is crucial for preventing vision impairment. The MobileNetV2’s lightweight and efficient design enables accurate and scalable classification of eye diseases, even with variable image quality from smartphone cameras. This paper provides an overview of the prevalence of cataracts and pterygium, summarizes prior work, and presents experimental results demonstrating MobileNetV2’s high performance in detecting both diseases. For pterygium classification, MobileNetV2 achieved its best performance with the Adam optimizer and a batch size of 10, delivering 97.37% accuracy, 96.05% sensitivity, and the highest AUC of 99.41%. It also demonstrated exceptional computational efficiency, completing training in just 2.13 min with Adam and Batch Size 32, the shortest training time across all configurations. The network exhibited consistent performance with only minor declines as the batch size increased. For cataract patch classification, MobileNetV2 also performed strongly, achieving 95.44% accuracy, 95.78% sensitivity, and an AUC of 99.19% with Adam and Batch Size 10. Additionally, it completed training in the shortest time of 7 min, making it highly efficient for resource-constrained environments. The findings support the integration of smartphone imaging and deep learning as a cost-effective solution for ophthalmological diagnostics.