<p>Pharmacometrics, as the core discipline of model-informed drug development paradigm, quantitatively characterizes drug exposure-response dynamics using mathematical models. However, its traditional methodology, represented by nonlinear mixed-effects modeling, is inherently resource-intensive and often struggles to fully capture complex biological variability. The rapid advancement of artificial intelligence technologies offers a transformative data-driven paradigm, providing superior efficiency and predictive accuracy. Yet, the artificial intelligence approaches suffer from the “black box” constraint. This review detailed the deep, complementary integration of artificial intelligence and pharmacometrics, demonstrating how this synergy is necessary to satisfy the complex, triple demands of accuracy, efficiency, and regulatory explainability. We systematically explored innovative applications across the entire pharmacometrics workflow, including model optimization, covariate screening, virtual population generation, etc. In each section, we also presented specific application cases or workflows. Furthermore, we outlined current challenges in the implementation of artificial intelligence and proposed a vision for interdisciplinary and cross-institutional collaboration.</p>