The innovations in artificial intelligence (AI) are transforming genetic editing by making it more accurate and effective. Some technologies associated with AI supplement the current practices, and others provide a solution to previously existing problems. AI-powered assistants help popularize guide RNA (gRNA) editing, making it more specific. AI can also reduce off-target edits by foreseeing editing and repairing pathways to enhance the safety and familiarity of genetic applications. Advancing methodologies such as base and prime editing have shown that more complex edits are possible and allow for single-base fixes without creating double-strand fusion. Capabilities of AI-based gene editing in agriculture range widely, with potential returns of increased crop yields, disease-protected crops, and crops able to adapt to changing environments. The developments are vital toward dealing with food security in a world affected by climate change and an increase in population. However, there is a technical gap in implementing AI in genetic editing, which includes the problem of good datasets and opaque algorithms. In the future, we are likely to see an even more intimate association between AI and gene editing, especially as they incorporate multi-omics data and give an overall picture of genetic interactions. Also, it is important that automation in the field of gene editing is bound to make the work more efficient in research and labs, so that the respective field proceeds at a fast pace.