Arthritis, which includes osteoarthritis (OA), rheumatoid arthritis (RA), and other inflammatory joint diseases, is a leading cause of pain, disability, and a lower quality of life worldwide. Early diagnosis and ongoing disease monitoring can be tough because arthritis shows up in many different ways. Clinical evaluations can also be quite subjective. In recent years, Artificial Intelligence (AI) has become a valuable tool in rheumatology. It helps analyze complex and multi-dimensional data, including medical images, electronic health records (EHRs), molecular biomarkers, and data from wearable sensors. Improvements in deep learning, radiomics, natural language processing (NLP), and machine-learning models have led to major advancements. These include better early detection of joint damage, predicting disease progression, identifying flares, and forecasting how individuals will respond to treatment. Despite these advances, challenges remain. Data variability, lack of transparency in decision-making, algorithmic bias, and difficulties in integrating AI tools into clinical workflows still hinder widespread use. This review gives an updated look at AI applications in arthritis from 2023 to 2025. It discusses their clinical importance, examines existing limitations, and outlines key steps needed to effectively incorporate AI innovations into everyday rheumatology practice.