Artificial intelligence (AI) is transforming pharmacometrics by integrating data-driven algorithms with mechanistic modeling approaches such as physiologically based pharmacokinetic (PBPK) and population PK models. These approaches are increasingly important for emerging drug modalities and model-informed drug development (MIDD).This study highlights how AI-enabled/AI-powered pharmacometric approaches can address challenges in modern drug development and support biologics development. Applications of AI in pharmacometrics were reviewed across both top-down and bottom-up modeling frameworks. In population modeling, AI enables automated covariate identification, parameter estimation, and model selection. In PBPK modeling, AI improves parameter prediction and supports digital twin development using in vitro and microphysiological system data. AI integration enhanced predictive performance and decision support in drug development. A semi-mechanistic PBPK modeling framework was established as an AI-expandable platform for biologics development. Also RF and XGBoost ML models were developed to explore potential role in key biomarker selection for response for QSP modeling. Integration of AI with pharmacometrics can accelerate MIDD and improve translational modeling for biologics. Such AI-powered pharmacometrics frameworks enable the use of accumulated experimental and clinical data available to better guide future biologic drug development for the prediction of clinical response.