According to the World Health Organization (WHO) touchstones of 2022, Tuberculosis is the second dominant disease after COVID-19. Around one-fourth of the comprehensive population is ascertained to have tuberculosis. Timely detection and prevention of tuberculosis is a must to overcome its harmful effects. The method most often used in ascertaining whether a patient has tuberculosis, is examining his or her sputum sample. In the process, the isolation of the bacilli is done manually, and hence it is prone to error. Segmentation illustrates and enlightens objects or particles within an image, thus extracting the Region of Interest (ROI). The contemplated study uses TransUNet architecture to segment tuberculosis bacilli from sputum images to increase diagnostic accuracy and performance. The attention mechanism used in the TransUNet model helps to identify the spatial hierarchies present in image. It is an extremely tough task for naive or traditional segmentation algorithms to deal with the inherent complexity of sputum images. Hence, this study introduces an approach to capture the intrinsic features and dependencies needed to segment mycobacterium or TB bacilli by leveraging the TransUNet model. The model achieved an average Dice Score of 92.795%, a mean Intersection over Union (IoU) of 88.845%, and a segmentation accuracy of  99.19% on the Mosaic and Ziehl-Neelsen datasets. These results surpassed several existing state-of-art methods like UNet, clustering and thresholding, depicting the superior capability of TransUNet in segmenting the TB bacilli. It deepens the potentiality of transformer-based CNN models, especially TransUNet, for improving the diagnosis of tuberculosis and supporting disease management.