Tuberculosis remains a significant health burden, especially in low and middle-income countries, contributing to substantial morbidity and mortality rates. Amid global efforts to reduce tuberculosis incidence and deaths, the COVID-19 pandemic has hindered progress achieved over the years. The severity assessment of tuberculosis patients post-diagnosis is crucial for effective treatment planning due to the disease’s complexity and diverse outcomes. Recent advancements in artificial intelligence (AI) have offered potential aids for healthcare professionals in tuberculosis treatment decisions, aiming to enhance patient outcomes and optimize healthcare resource allocation. In this context, the TITO application, utilizing an SVM-based machine learning (ML) model, was developed to predict tuberculosis prognosis. In this paper, we evaluate TITO usability among healthcare professionals in Amazonas, Brazil, using the System Usability Scale (SUS) questionnaire and results yielded a SUS score of 83.95%. These findings suggest that TITO exhibits high usability among healthcare professionals, making it a promising tool for facilitating tuberculosis prognosis prediction. Further studies are warranted to evaluate the clinical effectiveness of TITO in real-world settings.