<p>Artificial intelligence (AI) is rapidly transforming pharmaceutical and biopharmaceutical research by accelerating drug discovery, improving nanomedicine design, and enhancing therapeutic outcomes. AI-driven approaches such as machine learning (ML), deep learning, and quantitative structure–activity relationship (QSAR) modeling enable efficient target identification, prediction of drug toxicity, and optimization of molecular structures, thereby reducing the time and cost associated with traditional drug development. In addition, the integration of AI with nanotechnology has facilitated the design of advanced drug delivery systems, including lipid-based, polymeric, carbon-based, and inorganic nanoparticles capable of targeted delivery and controlled drug release. AI also supports the development of personalized therapeutic strategies by analyzing patient-specific data and disease microenvironments, improving treatment precision and minimizing adverse effects. Furthermore, AI-driven tools such as natural language processing and biomedical data mining assist in extracting knowledge from large biomedical datasets and support decision-making in research and regulatory processes. Despite these promising developments, challenges related to data quality, model transparency, and regulatory acceptance remain. This review highlights the applications of AI in drug discovery, nanomedicine, and pharmaceutical development while discussing current limitations and future perspectives for the integration of AI in precision medicine and biotechnology.</p> Graphical Abstract <p></p>