Antimicrobial resistance (AMR) poses a significant global health threat, with projections indicating it could surpass cancer in mortality rates by 2050 if left unaddressed. Optimizing antimicrobial dosing is critical to mitigate resistance and improve clinical outcomes. Traditional approaches, including population pharmacokinetics (PK) models and Bayesian estimation, are limited by mechanistic hypothesis requirements and complexity. Artificial intelligence (AI) and machine learning (ML) offer transformative solutions by leveraging large datasets to predict drug exposure accurately, refine sampling strategies, and enable real-time dose adjustments through therapeutic drug monitoring. This review highlights the role of ML models, in managing PK and pharmacodynamic variability across diverse patient populations. AI models often equal or outperform traditional methods in achieving therapeutic targets while minimizing toxicity, as demonstrated in some case studies involving ganciclovir, vancomycin, and daptomycin. Despite challenges such as data quality, interpretability, and integration with clinical workflows, AI's dynamic adaptability and precision underscore its potential. Future directions emphasize integrating multi-omics data, developing bedside decision-support tools, and expanding AI applications to broader drug categories and populations. Continued research and clinical validation are essential to harness AI's full potential in advancing precision medicine and combating AMR effectively.