Researchers have integrated artificial intelligence (AI) and deep learning techniques to enhance precision and predictability in gene editing. A study from the University of Zurich introduced 'Pythia,' an AI tool that designs microhomology-based DNA repair templates, improving CRISPR/Cas9 genome integrations across multiple loci in human cells and animal models. Parallel advancements include AI-guided design of small enzymes for bioprocessing and hybrid ML models that enhance monoclonal antibody design predictions, offering faster and more reliable biotechnological developments. These innovations mark significant strides towards safer and more effective genome editing and biologic drug development.