Tuberculosis is still one of the most lethal infectious diseases in the world, and hence early treatment is necessary through high-end, effective diagnostic methods. In the paper, a novel framework for combining deep learning networks and the Chameleon Swarm Algorithm (CSA) is proposed for TB classification from chest radiographs. Through the use of CSA in optimization of feature selection, the deep neural network performed excellently, on a 10,000-labeled chest X-ray database—registering an impressive 98.7% accuracy, 97.9% precision, and 99.1% recall. The system was more efficient than baseline approaches, with 15% greater accuracy and 12% fewer false positives, detection being more believable. Moreover, the solution, being as proposed as outlined, is automatic and scalable and therefore can be an efficient solution to be implemented within resource-limited healthcare settings. With the potential to enhance early diagnosis and reduce diagnostic mistakes, the solution has a great promise for transforming the world's control against TB—unleashing the possibility of more rapid, precise, and cheaper diagnostic tests to combat one of the most recalcitrant diseases in the world.