The therapeutic management of chronic diseases—including diabetes, cardiovascular disorders, oncology, and autoimmune conditions—remains constrained by conventional fixed-dosing regimens that fail to account for substantial interindividual pharmacokinetic and pharmacodynamic variability. Pharmacometric modeling provides a quantitative framework for characterizing drug exposure–response relationships and optimizing individualized therapy. The integration of artificial intelligence with pharmacokinetic–pharmacodynamic modeling represents a transformative paradigm shift toward model-informed precision dosing. This review examines the mechanistic foundations of pharmacokinetic–pharmacodynamic modeling, including compartmental analysis, population approaches, Emax models, and indirect response models, as foundations for personalized dose individualization. Artificial intelligence-driven strategies—encompassing supervised learning algorithms, neural networks, reinforcement learning, and hybrid mechanistic-machine learning architectures—enhance predictive accuracy by capturing nonlinear relationships and processing multimodal patient data. Bayesian forecasting frameworks augmented with machine learning enable adaptive dosing algorithms that incorporate therapeutic drug monitoring data in real time. Clinical applications across chronic disease domains demonstrate improved outcomes: insulin optimization in diabetes through glucose–insulin dynamic modeling, anticoagulant dose individualization in cardiovascular disease, and targeted therapy optimization in oncology and autoimmune disorders where narrow therapeutic indices demand precise exposure control. Comparative evaluation reveals that artificial intelligence-augmented approaches achieve superior predictive accuracy and safety outcomes compared to traditional methods, though challenges including data heterogeneity, model validation requirements, and regulatory acceptance persist. Future directions include hybrid mechanistic-artificial intelligence architectures that preserve physiological interpretability while leveraging data-driven flexibility, real-time clinical decision support system integration, and personalized digital twin platforms. The convergence of pharmacometrics and artificial intelligence holds substantial promise for transitioning chronic disease management from empirical dosing to precision pharmacotherapy.