Computational ghost imaging (CGI) achieves high-precision image reconstruction by performing second-order correlation operations between modulated optical fields and the corresponding intensity information, overcoming the limitations of traditional "point-to-point" imaging methods. This technique can decouple high-resolution object images from one-dimensional intensity signals, demonstrating high sensitivity and strong anti-interference capabilities. It holds broad application prospects in fields such as medical imaging, microscopic imaging, and LiDAR. This paper provides a detailed overview of the development and applications of traditional CGI, compressed sensing-based CGI, and deep learning-based CGI. It also analyzes the algorithms of each type of CGI and discusses the feasibility of applying large language model to CGI.