Objective: Biologic disease-modifying antirheumatic drugs (bDMARDs) offer promising results for rheumatoid arthritis (RA) patients in general, but a substantial percentage of patients do not respond to them. It is important to predict the response before the treatment so that unnecessary adversities for the patients and costs for the healthcare system can be avoided. This study aims to develop a machine learning (ML) model that works with readily-available demographic and clinical factors for prediction of response to bDMARDs, and discusses additional non-pharmacological practices. Methods: Several ML models were tested in 190 RA patients from Turkey, and the logistic regression model was found to be superior. The relation between long-term and short-term responses were also analyzed. Results: Predictors of the logistic regression model were age, sex, coronary artery disease, spine surgery, steroid treatment, sulfasalazine treatment and baseline health assesment questionnaire score. The model displayed 79.5% accuracy and an area under receiver operating characteristic curve of 0.82. 87% of the patients who were goodresponders in six-month follow-up were also good responders in oneyear follow-up. Among non-responders in six-month follow-up, 75% were also non-responders in one-year follow-up. Conclusion: Making the prediction at an early stage is crucial for the patients as well as the healthcare system. However, it is equally important to determine how to proceed with the patients who are unlikely to respond to bDMARDs. Current literature does not adequately answer this question. Additional treatment options and multiple evaluation criteria for these options should be considered; multiple criteria models can provide useful decision support for this purpose.