Tuberculosis (TB) remains a pressing global health issue, requiring timely and accurate diagnosis to prevent its spread and ensure effective treatment. In this study, we explore the po- tential of deep learning and computer vision to enhance TB detection using readily accessible tools like smartphone cameras. Specifically, we leverage the YOLOv8 object detection algo- rithm to analyze images of microscopic slides stained for TB, captured via smartphones. The dataset used in this study consists of 1,224 annotated images sourced from Roboflow, divided into training (861 images), validation (244 images), and test (119 images) sets. Our YOLOv8 model was trained to identify TB bacteria within these images, employing various data augmentation techniques to improve generalization. The model was trained over 100 epochs, and we applied hyperparameter tuning to optimize performance. The training process took approximately 0.826 hours. After training, the model achieved a precision of 72.7%, a recall of 78.7%, and a mean average precision (mAP) of 82.7% at an IoU threshold of 0.5. Additionally, the overall mAP (IoU from 0.5 to 0.95) was 41.5%. The final model size, after stripping the optimizer, was reduced to 22.5MB. These results demonstrate that YOLOv8 is well-suited for TB detection, offering reliable performance with potential real-world applications, especially in remote areas where access to specialized diagnostic equipment is limited. By incorporating YOLOv8 into a smartphone- based diagnostic tool, we propose a more accessible solution for TB detection that could as- sist healthcare workers in resource-constrained settings. This approach not only increases the speed of TB diagnosis but also helps address the challenges associated with traditional meth- ods, which are often time-consuming and require trained personnel. Our findings suggest that YOLOv8, combined with the ubiquity of smartphones, can play a crucial role in advancing TB diagnostics globally.