<p>Model-informed drug development (MIDD) plays an important role in pharmacometrics by leveraging mathematical models to optimize drug dosing strategies. Traditional methods such as nonlinear mixed effects modeling (NONMEM) have long been the gold standard in population pharmacokinetic (PPK) modeling. However, the development of artificial intelligence (AI) presents a potential improvement in predictive performance and computational efficiency. This study evaluates the effectiveness of AI-based MIDD methods for PPK analysis by comparing them against traditional nonlinear mixed-effects (NLME)-based methods (e.g., NONMEM). We tested five machine learning (ML) models, three deep learning (DL) models, and a neural ordinary differential equations (ODE) model on both simulated and real clinical datasets under different scenarios, assessing predictive performance with metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Simulated datasets with known ground truth were created using a two-compartment model, while the real clinical dataset included data from 1,770 patients pooled from multiple clinical trials. Results indicate that AI/ML models often outperform NONMEM, with variations in performance depending on model type and data characteristics. Neural ODE models showed good performance, providing strong performance and explainability with large datasets. These findings underscore the potential of AI/ML methodologies to complement or enhance traditional PPK modeling approaches in MIDD, highlighting their applicability in future pharmacometrics workflows.</p>