 The past few years have seen a surge in the use of deep learning techniques in the field of genetic engineering. Specifically, deep learning methods are being used to improve the accuracy of predictions regarding the activity of guide RNAs, GRNAs, which are essential components of the CRISPR-Cas system. This review provides an overview of the current state of research in this area, focusing on the integration of deep learning with the CRISPR-Cas system. It also discusses the potential impact of these techniques on the effectiveness of the system, as well as its future directions. This article was authored by Minhiak Lee.