In recent years, the rapid development of image acquisition equipment has greatly enriched the types and quantities of images. As the key of image analysis and processing, image registration technology has become increasingly important in the fields of image fusion, pattern recognition and computer vision, and how to register images with high accuracy and in real time has become the focus of research in related fields. At the same time, with the rapid development of deep learning techniques, convolutional neural networks show unique advantages in image representation and feature extraction. The aim of this work is to provide a systematic review of research on image registration using deep learning techniques. By discussing typical deep learning-based image registration methods from deep iterative registration, fully supervised image registration, weak/dually supervised image registration, and unsupervised image registration, we highlight common challenges faced by related researchers, and explore possible future research directions to address these challenges.