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The initial step in diagnosing tuberculosis involves the microscopic examination of sputum samples using acid-fast staining to identify bacilli. However, this conventional method is labor-intensive, requires specialized expertise, is susceptible to errors, and has limited sensitivity. Research literature indicates that deep learning models demonstrate significant potential for detecting acid-fast bacilli (AFB) in sputum smear preparations. This study investigates the effectiveness of deep learning methods in identifying AFB within sputum smear samples. Our objective was to assess the performance of these models in tuberculosis diagnosis based on microscopic examination and to identify improvements they could bring in terms especially of sensitivity and availability within this field. We employed several transfer learning models: DenseNet201, ResNet101V2, Xception, InceptionResNetV2, and InceptionV3. In order to determine the effectiveness of these models, basic performance metrics such as accuracy, recall, precision, and F1 score were used. Among the transfer learning models we recommended, the InceptionV3 and Xception models exhibited the highest performance, achieving 99.00% high performance across all evaluation metrics. Our findings underscore that deep learning models can be effectively utilized for rapid and accurate detection of Mycobacterium tuberculosis in acid-fast stained sputum preparations.