Artificial intelligence (AI) methods are increasingly transforming traditional pharmacokinetic (PK) and pharmacodynamic (PD) modeling, offering scientifically promising but clinically debated alternatives to established approaches such as population PK, nonlinear mixed-effects, and physiologically based PK (PBPK) models. This mixed-methods review synthesized both quantitative performance data and qualitative perspectives on AI in PK/PD modeling, encompassing neural network methods, Bayesian approaches, population PK/PD modeling, PBPK integration, and clinical translation. Using a convergent segregated mixed-methods design, the review integrated comparative model performance evidence with insights into implementation barriers and enablers, employing narrative weaving to link predictive accuracy with themes of interpretability, regulatory considerations, and workflow readiness. Quantitative findings indicated that neural network approaches often matched or outperformed traditional PK predictions in richly sampled datasets, while qualitative analyses highlighted that uncertainty quantification, explainability, clinical workflow integration, and prospective validation remain key challenges to adoption. Overall, AI is not replacing pharmacometrics but is expanding its methodological frontiers, with hybrid integration of neural, Bayesian, population, and mechanistic approaches—underpinned by rigorous clinical validation—emerging as the most promising path forward.