This study analyzed 8,543 rheumatoid arthritis patients from a national treatment registry who started biologic or targeted synthetic therapies between 2002 and 2023. Among them, 641 eventually met the definition of difficult-to-treat rheumatoid arthritis, while 1,825 achieved sustained remission. Remission was defined using low symptom scores and minimal joint swelling recorded across 2 visits 12 weeks apart. The researchers examined patient data from the start of treatment and from 1 and 2 years before patients developed difficult-to-treat disease. Several machine learning models were tested, including logistic regression, support vector machines, random forests, and XGBoost. Their predictive accuracy ranged from 0.606 to 0.747, and AUC values ranged from 0.656 to 0.832, showcasing moderate to strong predictive capabilities. The explanatory analysis identified disease activity scores, inflammation markers, self reported functional ability, and treatment duration as the most influential features. These measures often began shifting well before patients showed clear clinical deterioration. The findings show that small changes in routine clinical indicators can signal risk long before difficult-to-treat disease becomes established.