Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions Artificial intelligence (AI) is transforming genome editing (GED) technologies, particularly CRISPR-based methods, by enhancing precision, efficiency, and affordability. AI models, such as DeepCRISPR, CRISTA, and DeepHF, are used to design guide RNAs (gRNAs) for CRISPR-Cas systems, considering factors like genomic context, Cas protein type, and on/off-target scores. These models optimize GED techniques like base editing, prime editing, and epigenome editing, enabling precise DNA modifications without relying on homology-directed repair. AI also aids in personalized medicine by analyzing genomic data to identify mutations and biomarkers for diseases like cancer and Alzheimer's. However, challenges remain, including high costs, off-target effects, and the need for efficient delivery methods. This review explores AI's role in improving CRISPR-based GED, addresses existing challenges, and discusses future research directions. AI's integration with GED opens new possibilities in genetics, biomedicine, and healthcare. The review highlights the importance of AI in designing gRNAs, improving editing outcomes, and personalizing treatments. It also discusses the need for further research in AI-driven GED, including optimizing deep learning models, transfer learning for small datasets, and uncertainty quantification. The review emphasizes the potential of AI in advancing GED technologies and addressing challenges in clinical applications.Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions Artificial intelligence (AI) is transforming genome editing (GED) technologies, particularly CRISPR-based methods, by enhancing precision, efficiency, and affordability. AI models, such as DeepCRISPR, CRISTA, and DeepHF, are used to design guide RNAs (gRNAs) for CRISPR-Cas systems, considering factors like genomic context, Cas protein type, and on/off-target scores. These models optimize GED techniques like base editing, prime editing, and epigenome editing, enabling precise DNA modifications without relying on homology-directed repair. AI also aids in personalized medicine by analyzing genomic data to identify mutations and biomarkers for diseases like cancer and Alzheimer's. However, challenges remain, including high costs, off-target effects, and the need for efficient delivery methods. This review explores AI's role in improving CRISPR-based GED, addresses existing challenges, and discusses future research directions. AI's integration with GED opens new possibilities in genetics, biomedicine, and healthcare. The review highlights the importance of AI in designing gRNAs, improving editing outcomes, and personalizing treatments. It also discusses the need for further research in AI-driven GED, including optimizing deep learning models, transfer learning for small datasets, and uncertainty quantification. The review emphasizes the potential of AI in advancing GED technologies and addressing challenges in clinical applications.