Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by synovitis and progressive joint destruction, leading to a high disability rate and complex complications that severely compromise patients' quality of life. Although traditional diagnosis and treatment have been shown to significantly enhance patient prognosis, challenges such as the difficulty of early diagnosis, substantial individual variability in treatment response, and the high prevalence of complications, including cardiovascular diseases and interstitial lung disease, continue to be focal points in clinical practice. Recent rapid advancements in artificial intelligence (AI) and machine learning (ML) technologies offer novel opportunities to overcome these bottlenecks in RA management. By deeply mining RA multimodal medical data, such as patient imaging, genomics, and electronic health records, ML models have shown potential advantages in early diagnosis, stratified management of treatment response, and modeling of complication risk, such as early warning of cardiovascular events. However, barriers such as data heterogeneity, limited model interpretability, and challenges in clinical translation hinder widespread adoption. This paper systematically reviews the latest research progress in applying ML to RA diagnosis and treatment, aiming to provide a theoretical foundation and practical reference for realizing ML-driven precision medicine in RA.