Precision dosing aims to individualize drug therapy by tailoring dose regimens to patient-specific characteristics, disease states, and dynamic responses. Therapeutic drug monitoring (TDM) has traditionally relied on population pharmacokinetics, sparse sampling, and clinician experience to optimize efficacy and minimize toxicity, particularly for drugs with narrow therapeutic indices. The rapid evolution of artificial intelligence (AI), including machine learning (ML), deep learning (DL), and reinforcement learning (RL), has transformed the landscape of precision dosing and TDM by enabling real-time learning from large, heterogeneous clinical datasets. This review comprehensively discusses the state of the art in AI-driven precision dosing and TDM, including data sources, modeling approaches clinical applications, regulatory considerations, and integration into healthcare systems. Furthermore, unmet needs, ethical challenges, and future research directions are critically analyzed in the context of UGC-relevant biomedical and pharmaceutical research.