In recent years, expanding uses of artificial intelligence (AI) and machine learning have revolutionized pharmaceutical research and development, allowing us to harness multi-dimensional biological and clinical data from experimental to real-world settings (ML). Precision medicine discovery and development, from target validation to medication optimization, is driven by patient-centered iterative forward and reverse translation. As evidenced by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome, the integration of advanced analytics into the practise of Translational Medicine is now a critical enabler for fully exploiting information contained in diverse sources of big data sets such as “omics” data. In this article, we provide an overview of machine learning (ML) applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, and dose), and discuss how they can alter the science and practise of the discipline. Model-informed drug discovery and development will be revolutionised if ML approaches are integrated into the science of pharmacometrics. Finally, we believe that cross-functional team activities involving clinical pharmacology, bioinformatics, and biomarker technology experts are critical to realising the promise of AI/ML-enabled Translational and Precision Medicine.