Predicting attitudes towards people with tuberculosis is a solution for preserving public health and a means of strengthening social ties to improve resilience to health threats. The assessment of attitudes towards the sick in general is essential to understand the educational level of a given population and to measure its resilience in contributing to the health of all within the framework of community life. The case of tuberculosis is chosen in this study to highlight the need for a change in attitudes, particularly due to the preponderance of this disease in Africa. While it is clear that attitudes influence the organization of individuals and community life, it remains a challenge to put in place an effective mechanism for evaluating the metrics that contribute to determining the attitude towards people with tuberculosis. Knowledge of attitudes towards any disease is very important to understanding collective values on this disease, hence the need to predict attitudes in the case of tuberculosis in favor of health education for all social strata while targeting areas of training not yet explored or requiring capacity building among populations. Changing attitudes towards tuberculosis patients will contribute to preserving public health and will help reduce stigma, improve understanding of the disease and encourage supportive and preventive behaviors. Achieving these changes involves dismantling stereotypes, improving access to care, mobilizing the media and social networks, including people with TB in society and strengthening the commitment of public authorities. The approach adopted consists of assessing the state of attitude towards tuberculosis patients at a given time and in a specific space based on the characteristics of the different social strata living there. An analysis of several metrics provided by machine learning algorithms makes it possible to identify differences in attitudes and serve as a decision-making aid on the strategies to be implemented. This work also relies on the investigation and analysis of historical trends using machine learning algorithms to understand population attitudes towards tuberculosis patients.