CRISPR has enabled researchers to introduce precise modifications into the genome, achieving desired traits in plants. It offers unprecedented control over DNA and RNA editing in plants with precision, flexibility, and versatility. Although the CRISPR system contains three components, including gRNA, Cas nuclease, and a PAM sequence to introduce the (double-strand break) DSB in the genome, gRNA holds a key importance in driving Cas nuclease to the target site. Over the past decade, several bioinformatics and machine learning tools have been developed to design efficient gRNAs for various applications of CRISPR–Cas technology. In this chapter, we highlight the importance of gRNA in CRISPR–Cas. Similarly, we also discuss different deep learning tools to design an efficient gRNA for different CRISPR applications. In addition, we also summarize the integration of machine learning and AI tools to improve CRISPR–Cas applications for gene editing. We also show how the integration of AI with CRISPR–Cas is influencing the gRNA design in different CRISPR applications. Finally, we summarize the changing dynamics of gRNA design with applications of AI in CRISPR–Cas studies.