An innovative solution for the early detection of tuberculosis (TB) using machine learning is introduced, specifically through the analysis of cough sound recordings. TB remains a leading cause of death worldwide[1], and early diagnosis is crucial to preventing its spread. Traditional TB screening methods are often costly, complex, and unavailable in resource-limited settings, leading to delayed diagnoses and increased transmission. To address this challenge, a machine learning-based system was developed to analyze cough sound samples and detect the likelihood of TB. The system leverages Convolutional Neural Networks (CNN) for accurate classification, achieving an impressive 97% accuracy on the TBScreen dataset. The primary feature of this solution is its simplicity and accessibility, enabling real-time TB detection through a user-friendly web application.