Pomegranates are highly valued horticultural crops, yet their market potential is often constrained by variations in quality and vulnerability to diseases. Traditional approaches to grading and disease identification are labor-intensive, error-prone, and unsuitable for large-scale operations. To overcome these limitations, this study introduces an automated dual-purpose framework for pomegranate quality grading and disease detection using deep learning and image processing. Two different datasets were used: one comprising twelve quality categories defined by fruit size, color, and surface characteristics, and another covering five disease classes—Alternaria, Anthracnose, Bacterial Blight, Cercospora, and Healthy. Convolutional Neural Network (CNN) models were trained with extensive augmentation techniques and refined through hyperparameter tuning. The proposed framework achieved 96% accuracy in quality classification and 98.5% accuracy in disease detection, surpassing conventional models such as Random Forest, SVM, and transfer learning baselines (ResNet50, InceptionV3, VGG16). These findings highlight the system’s robustness and scalability, making it a dependable solution for automated fruit sorting and disease monitoring.