Background: Antimicrobial resistance (AMR) among ESKAPE pathogens—Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.—represents a major global health threat and accounts for a substantial proportion of healthcare-associated infections. Their genomic plasticity and adaptive regulatory responses facilitate the rapid emergence and dissemination of resistance and virulence determinants. Artificial intelligence (AI) has emerged as a powerful approach for analyzing large-scale biological datasets and identifying molecular signatures associated with antimicrobial resistance and pathogenicity. Objectives: This review examines AI-driven frameworks for predictive target discovery in ESKAPE pathogens, focusing on approaches that leverage genomic and transcriptomic data and extend toward the integration of additional omics layers within network-based and systems-level modeling frameworks. We discuss how AI methods are evolving beyond phenotypic prediction toward more biologically interpretable inference for prioritizing resistance mechanisms, virulence determinants, and candidate antimicrobial targets. Conclusions and Future Directions: Current AI applications exploit genomic, transcriptomic, and network-level data to prioritize resistance and virulence determinants and to support antimicrobial discovery, including small molecules and antimicrobial peptides. However, integrative multi-layer modeling and comprehensive experimental validation remain limited. Future advances will depend on improved integration of complementary biological data, enhanced model interpretability, and robust translational validation frameworks to enable clinically actionable AI-guided novel pathogen-targeted next-generation diagnostics, therapeutic and stewardship strategies against ESKAPE pathogens.

Background/Objectives: Antimicrobial resistance (AMR), driven by high-risk bacterial pathogens, is a major healthcare threat. This scoping review mapped artificial intelligence/machine learning (AI/ML) and computational approaches integrated with whole-genome sequencing (WGS), genomic surveillance, rapid typing, epidemiological data, or clinical metadata for early warning of AMR outbreak clones. Methods: Following PRISMA-ScR guidance and the Population–Concept–Context (PCC) framework, PubMed/MEDLINE, Scopus, and Web of Science were searched for English-language studies published between 2010 and 2026. Eligible studies addressed AI/ML or computational approaches for AMR outbreak detection, clone surveillance, transmission analysis, or infection prevention and control (IPC). Results: Thirty-eight studies were grouped into five domains: genomic surveillance; rapid typing; resistance, risk-factor, and lineage prediction; transmission reconstruction; and IPC-oriented genomic epidemiology. AI/ML supported automation, isolate prioritization, typing triage, prediction, transmission modelling, and electronic health record (EHR)-linked route identification. Conclusions: AI/ML may enhance WGS-based AMR surveillance, but validation, dataset dependence, heterogeneity, and limited IPC outcome reporting remain key gaps.