Journal of Medicine and Health Research

Antimicrobial resistance (AMR) has emerged as one of the most pressing global public health challenges, reducing the effectiveness of existing antimicrobial treatments and increasing the burden of infectious diseases worldwide. The development of resistance is driven by complex ecological interactions, genetic transfer, and adaptive biochemical processes within microbial communities. However, current gene-based analyses alone are insufficient to fully explain these mechanisms, necessitating a broader investigation of biochemical pathways and molecular networks involved in resistance regulation. This study explores the potential of Artificial Intelligence (AI) in advancing the understanding of AMR through the analysis of large-scale microbiome and multi-omics datasets. Machine learning, deep learning, and network-based modeling approaches are considered for their ability to detect hidden biological patterns, predict resistance-associated pathways, and model microbial ecosystem behavior.AI-based approaches demonstrate significant potential in transforming AMR research from traditional descriptive analysis to predictive and interpretive modeling. These methods enhance the ability to identify resistance mechanisms, forecast evolutionary trends, and improve the understanding of microbial interactions at a systems level. Despite these advantages, challenges such as data quality limitations, lack of model interpretability, inadequate standardization, and unequal access to computational resources remain significant barriers. Ethical concerns related to data governance and clinical implementation must also be addressed. Future advancements in explainable AI, integrated multi-omics analysis, and robust computational frameworks are expected to improve predictive accuracy and support better diagnostic and treatment strategies. These developments hold strong potential for strengthening global efforts against antimicrobial resistance.