Introduction: Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or "omics" data). Such approaches may improve the management of patients with RA. Areas covered: In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: "arthritis, rheumatoid" and "machine learning". Based on this search, the usefulness of ML methods for RA diagnosis, monitoring and prediction of response to treatment and RA outcomes, is discussed. Expert opinion: ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers and physicians is therefore required to move this field forward.