In recent years, there has been an enormous interest in using deep learning to classify underwater images to identify various objects, such as fishes, plankton, coral reefs, seagrass, submarines, and gestures of sea divers. This classification is essential for measuring the water bodies' health and quality and protecting the endangered species. Furthermore, it has applications in oceanography, marine economy and defense, environment protection, underwater exploration, and human-robot collaborative tasks. This article presents a survey of deep learning techniques for performing underwater image classification. We underscore the similarities and differences of several methods. We believe that underwater image classification is one of the killer application that would test the ultimate success of deep learning techniques. Toward realizing that goal, this survey seeks to inform researchers about state-of-the-art on deep learning on underwater images and also motivate them to push its frontiers forward.