Asian Journal of Advances in Research

Multidrug-resistant organisms are increasingly difficult to manage because therapeutic scarcity, delayed susceptibility information and antimicrobial-selection pressure interact at the bedside. At the same time, artificial intelligence (AI) is entering infectious-disease practice through resistance prediction, rapid diagnostic interpretation, stewardship triage and drug discovery. This critical narrative review evaluates whether these developments are converging into a clinically credible management strategy for multidrug-resistant bacterial infections or remain largely parallel technological advances. Literature published from 1 January 2015 to 17 July 2026 was identified through open scholarly databases and citation searching, with earlier seminal studies retained where necessary. Evidence was appraised according to study design, external validity, clinical relevance, comparator quality, model validation and maturity of translation. The strongest therapeutic evidence concerns mechanism-matched antibacterial agents, including newer β-lactam/β-lactamase inhibitor combinations, sulbactam-durlobactam and cefiderocol, although effectiveness varies by pathogen, resistance mechanism and infection context. Bacteriophages, CRISPR-based antimicrobials, microbiome manipulation and AI-discovered antibiotics remain less mature; their biological rationale is substantial, but clinical evidence is limited or inconsistent. Machine-learning approaches applied to mass spectrometry, whole-genome sequencing, microscopy and electronic health records can accelerate resistance or susceptibility prediction, yet transportability, calibration, data shift and the difference between predictive accuracy and patient benefit remain central limitations. AI-supported antimicrobial stewardship can prioritise reviews, personalise empiric therapy and support de-escalation, but most evidence is retrospective and prospective outcome trials are uncommon. Large language models are not sufficiently reliable for autonomous prescribing, especially in difficult-to-treat infections. The most defensible near-term model is therefore not autonomous AI-guided treatment, but a governed decision-support architecture linking rapid microbiology, mechanism-aware therapeutics and stewardship feedback. Future progress should be judged by prospective clinical outcomes, resistance ecology, implementation equity and safety rather than discrimination metrics alone.