AIM: To analyze the clinical value of predicting drug resistance in pulmonary tuberculosis patients based on improved machine learning models, and to build a visualization system for verification. METHODS: Retrospectively selected 1 025 pulmonary tuberculosis patients hospitalized in Zhuhai Sixth People's Hospital from March 2019 to March 2024 with drug sensitivity test results as the research object. According to the definition of drug-resistant tuberculosis, the patients were divided into 631 sensitive groups (drug sensitivity test results showed no drug resistance), 271 RR/MDR groups (meeting the definition of rifampicin resistant tuberculosis or multi drug resistant tuberculosis, but no drug resistance to any kind of fluoroquinolones), and 123 pre XDR groups (on the basis of multi drug resistant tuberculosis, and at the same time, drug resistance to any kind of fluoroquinolones). Analyze clinical data based on the improved machine learning model, help build a drug resistant tuberculosis prediction model, synchronously complete feature screening, conduct value analysis on the screened features, and build a visual system for verification. RESULTS: Three groups of patients with baseline data comparison shows: Age, Body mass index(BMI), basic treatment of classification, lung diseases, haemoptysis, second-line drug use history, damage to lung, with empty in all statistically significant difference between the three groups (P< 0.05); Based on the modified machine learning model, 8 variables were screened, which were history of second-line drug use, BMI, treatment classification, destructive lung, underlying lung diseases, cavitation, hemoptysis, and age. The modified machine learning model had the highest prediction accuracy compared with the traditional model, with AUC values of 0.9322 (RR/MDR prediction was positive class) and 0.9545 (pre-XDR prediction was positive class). CONCLUSION: The application of the improved machine learning model can help predict the occurrence of drug-resistant tuberculosis and assist the clinical formulation of more effective treatment plans.