Despite ongoing advances in medicine, Tuberculosis remains a critical health problem, especially in developing nations where the lack of appropriate diagnostic aids is prevalent [15]. Timely detection of the condition is key to successful management; however, visual analysis of chest radiographs can be laborious and subjective among specialists. Thus, this research proposes a machine learning framework for identifying TB from chest X-rays. The methodology entails applying transfer learning on a pre-trained Convolutional Neural Network (CNN) to categorize the input images into TB-positive and healthy subjects [1]. Public datasets will serve as the source of data for training and testing purposes, combined with preprocessing and data augmentation strategies to boost predictive power [10]. The efficacy of the algorithm will be evaluated by accuracy, precision, recall, and F1-score measurements. Moreover, the interpretability of the algorithm is enhanced using Grad-CAM visualization to pinpoint significant lung areas affecting the classifier\'s decision-making process [7].