In recent years, artificial intelligence (AI) has emerged as a powerful tool in healthcare and is becoming increasingly prevalent across all medical and paramedical disciplines. AI has numerous applications in pharmacology. This narrative review explores the increasing importance of AI in three key areas of pharmacology: pharmacokinetics (PK), pharmacodynamics (PD), and pharmacovigilance (PV), as well as pharmacology education. We conducted a literature review enhanced by the ARTIREV hybrid bibliometric tool to identify and analyze key advances, applications, and challenges with AI integration in this field. In PK, machine learning and hybrid approaches improve the prediction of individualized drug exposure, support model-informed precision dosing and handle irregular and sparse data through architectures such as recurrent neural networks and NeuralODEs. In PD, AI facilitates a shift towards an era of precision and personalized medicine by enabling the development of drug effect models and considering interindividual variability. It also makes it easier to implement adaptive dosing regimens that are tailored to various constraints. Regarding PV, AI enhances the detection of adverse drug reactions, the identification of safety signals at the population level and the assessment of preclinical toxicities through the analysis of unstructured data, particularly from electronic health records. Despite their potential, AI models face several significant limitations. These include the quality of training data, limited explainability due to the “black box” effect and a lack of external validation of the models developed. Altogether, this review emphasizes the role of AI in pharmacology and the necessity of training future professionals to ensure the safe and validated use of AI in personalized medical applications.