Tuberculosis (TB) bacteria may develop resistance to the drugs, which are used in TB treatment. Multidrug-resistant TB (MDR-TB) is a type of TB that does not respond to at least rifampicin and isoniazid, the 2 most powerful anti-TB drugs. MDR-TB requires a more compelling treatment and it is more difficult to diagnose. The experience of physician is the key factor in the success of MDR-TB diagnose. The existence of TB bacteria in the body can be observed relatively faster with a standard sputum smear however, drug-susceptibility tests require nearly 45 days. To cope with this infectious disease, it is vital to estimate the resistance in a newly diagnosed TB patient to plan the initialization of the treatment in the testing period. Herein, the purpose of this study is to build a framework and establish a mathematical model that will help decision makers (physicians) while estimating the risk of multidrug resistance when a new tuberculosis patient arrives, using intuitionistic fuzzy cognitive maps (IFCM). Intuitionistic fuzzy sets are utilized to reflect the decision makers' hesitancy degrees in the model.