Artificial intelligence and machine learning-driven smart nanomedicine strategies to combat antimicrobial resistance

Objective To explore the integration of artificial intelligence (AI), mainly machine learning (ML) and deep learning (DL), with nanotechnology, as a novel approach for countering antimicrobial resistance (AMR). Special emphasis will be given to nanoparticle (NP)-based antimicrobial development, improved resistance prediction, and smart, precision-based therapeutic strategies. Method This review evaluates recent advances in the integration of AI with nanotechnology to address AMR. Relevant literatures on ML and DL applications were analyzed, with emphasis on processing complex physicochemical, biological, and microbiological datasets. The review further examined AI-based prediction of NP responses, optimization of NP physicochemical properties, and identification of NP-microbial interactions. In addition, AI-enabled nano-biosensors for rapid, real-time detection of AMR pathogens were evaluated. Their applications in targeted antimicrobial drug delivery and biofilm disruption were also reviewed to identify emerging strategies for effective and precision-based AMR management. Result AI-nanotechnology integration demonstrated strong potential for designing and optimizing the next-generation antimicrobial nanomaterials. AI-based approaches improved prediction of microbe-NP interactions and optimization of physicochemical properties, while supporting targeted drug delivery and biofilm-disrupting therapies. AI-enabled nano-biosensors enabled rapid, accurate, and real-time detection of antimicrobial-resistant pathogens, promoting prior and more accurate interventions. However, challenges involving data quality, model interpretability, biological complexity, scalability, regulatory compliance, and clinical translation remained significant barriers to the widespread application of these technologies. Conclusion The integration of AI and nanotechnology offers a promising, innovative strategy to address the global challenge of AMR. By combining computational prediction, smart nanomaterial design, advanced diagnostics, and targeted antimicrobial delivery, this approach has the potential to transform infectious disease management. However, sustained and continued research, technological development, and effective clinical translation are essential to fully realize its potentialbesides strengthening global health security.