Artificial intelligence (AI) is increasingly reshaping the biomedical continuum, offering new capabilities from early discovery to patient care. This review explores current advances, starting with drug discovery, where AI aids in target identification, virtual screening, lead optimization, and prediction of pharmacokinetic and toxicological profiles, thereby reducing development costs and attrition rates. In preclinical and translational research, AI may support improvements in in vitro–in vivo correlations, physiologically based pharmacokinetic modeling, and the development of AI-assisted approaches that could complement selected animal studies. These applications may improve predictive reliability while also helping to address some ethical concerns related to preclinical evaluation. In clinical trials, AI may have a role in patient recruitment, adaptive trial design, dose prediction, and the analysis of real-world evidence. In clinical medicine and pharmacy practice, its use is also increasing across diagnostic support, clinical decision-making, pharmacovigilance, and supply chain management. These applications could improve the delivery of more personalized and accessible care, but this is not always straightforward, since the usefulness of AI depends on the quality of the available data, the validation process, and how the system is applied in real clinical settings. Several concerns, therefore, remain, particularly regarding transparency, algorithmic bias, data privacy, validation, and regulatory compliance. The review also discusses possible future directions, including precision medicine, digital twin technologies, and the integration of AI with emerging biotechnologies, while stressing the need for responsible, validated, and equitable use of AI in healthcare.