Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities

Antimicrobial resistance and persistent biofilm-associated infections have renewed interest in bacteriophages as alternatives or complements to conventional antibiotics. However, broader therapeutic adoption remains constrained by slow phage discovery, incomplete genome characterization, narrow host range, complex therapeutic matching, and manufacturing variability. Artificial intelligence (AI) offers computational approaches that may help address several of these limitations. This comprehensive narrative review discusses current AI applications across the bacteriophage pipeline, including metagenomic phage discovery, genome annotation, phage–host interaction prediction, personalized phage selection, cocktail optimization, and phage–antibiotic combination design. The review also examines AI-assisted synthetic biology approaches, including receptor-binding protein redesign, CRISPR-enabled engineering, generative genome design, and biosafety screening, as well as emerging applications in bioprocess optimization, yield prediction, purification analytics, quality assurance, and supply-chain management. Current evidence suggests that AI may accelerate phage identification, improve host-range prediction, support therapeutic optimization, and strengthen manufacturing consistency, potentially facilitating the transition of phage therapy from individualized rescue interventions toward more scalable antimicrobial platforms. Nevertheless, major limitations remain, including fragmented, taxonomically biased datasets; limited external validation; restricted interpretability; privacy concerns; biosafety oversight; and evolving regulatory frameworks. Future progress will depend on standardized datasets, multimodal validation, scalable manufacturing systems, experimental and clinical verification, and coordinated regulatory development.