ABSTRACT

Antibiotic resistance has become a growing global crisis, threatening to make once-curable infections increasingly difficult to treat and challenging the foundation of modern medicine. Bacteria have developed remarkable ways to survive antibiotic exposure, including producing enzymes that deactivate drugs, altering their target sites, reducing membrane permeability, and forming protective biofilms. Traditional methods of drug discovery and resistance detection are often slow and unable to keep pace with how quickly bacteria adapt. In recent years, artificial intelligence (AI) has emerged as a powerful tool to address this challenge. By analysing vast amounts of genomic, chemical, and clinical data, AI can predict resistance patterns, identify new antibiotic candidates, and design more effective treatment strategies. It is also revolutionizing rapid diagnostics, helping detect resistant infections within hours, and strengthening global surveillance by tracking emerging resistance trends. While challenges such as data limitations, lack of model transparency, and ethical concerns remain, the integration of AI with microbiology and clinical research offers a promising path forward. This review explores how AI is transforming our understanding of antibiotic resistance accelerating drug discovery, guiding personalized therapy, and offering innovative solutions to one of the greatest health threats of our time.