Nail diseases are disorders that can have serious effects on human quality of life. With the developing computational methods and technology, anomalies on the nail may be detected quickly and in a non-invasive way. This study proposes a model that provides better performance by combining the results of different deep learning networks with the ensemble learning method. The performance of 7 different deep learning architectures was examined using a database containing 17 disease classes. The proposed method achieved 75 % accuracy, resulting in significant increases in precision and recall metrics compared to individual deep-learning architectures. Thanks to a mobile application that will be developed, the proposed model for large-scale screening may be used as an assistive decision support system for medical professionals. When the results are observed, we predict that early detection of nail diseases (in a remote way) on the hand, which is one of our most used limbs, can reduce hospital visits and costs. In addition, the proposed method can be integrated into dermatoscopy devices used for skin diseases and mole analysis.