Dental panoramic radiographs are widely recommended for examining abnormalities in the teeth, jaws, and surrounding structures. They play a crucial role in diagnosing various dental and jaw-related conditions, such as impacted teeth, cysts, and tumors. Additionally, they can assess bone density changes, making them valuable for the early detection of systemic diseases like osteoporosis through opportunistic screening. Osteoporosis is often called a “silent dis- ease” because bone loss occurs gradually and painlessly, making it difficult to detect until bones become weak and fracture easily. In this research, a framework was developed to automate early osteoporosis detection using a Convolutional Neural Network based deep learning approach on dental panoramic radiographs, serving as an opportunistic screening tool. The study utilized panoramic radio- graphs from 195, which were annotated into two groups by an oral radiologist: normal (C1) and osteoporotic (C2 + C3). Region of Interest was extracted using U Net. Convolutional layers were trained on this dataset for feature extraction, and features were reduced using the proposed feature influence calculation method and heuristic feature selection method and two layers of dense layer was used for classification. This model was compared with state of art deep learning image classification algorithms like inception v3, DenseNet121 and ResNet50. The proposed framework achieved exceptional performance, with an accuracy of 94%. Precision, recall, and F1 score were recorded at 0.92, 0.95, and 0.93, respectively. The proposed algorithm offers an automated approach to predict osteoporosis as an opportunistic screening tool, enabling timely intervention.