Background/Objectives: Biosimilars are medicinal products derived from reference biologics and designed to demonstrate a high degree of similarity in quality, efficacy, safety, and immunogenicity. Machine learning (ML) and other artificial intelligence (AI) methodologies have emerged as important tools in this field in biosimilar research and development. This systematic review identifies ML applications throughout the biosimilar lifecycle while distinguishing them from the broader AI literature and from health technology evaluation, economic, and decision-analytic studies. Methods: Following PRISMA, records were retrieved from Scopus, PubMed, and Web of Science. After applying predefined inclusion and exclusion criteria, 44 original peer-reviewed studies were selected. Only studies that implemented a data-driven ML method for a biosimilar-relevant problem were included. Results: The review mapped AI applications at different stages of biosimilar development and characterized emerging trends and the types of methods used at each stage. Evidence indicates that the most mature empirical ML applications are concentrated in manufacturing optimization and analytical comparability, where supervised learning, ensemble models, and neural networks support process control, glycan or spectral analysis, and similarity assessment. By contrast, biosimilar-specific ML applications in clinical prediction and pharmacovigilance remain comparatively limited. Conclusions: These advances support the mission of biosimilars to provide affordable and high-quality biologic therapies. Using ML, developers can reduce timelines, reduce costs, and strengthen safety and efficacy assessments through the analysis of complex datasets that are difficult to address with traditional approaches. The main contribution of this review is to provide a clearer map of methodological maturity, translational relevance, and future opportunities for data-driven biosimilar development.
Duarte et al. (Fri,) studied this question.