New AI-powered tools are emerging as game-changers in the global fight against tuberculosis, offering faster, more accessible, and more accurate ways to detect, monitor, and prevent the disease. Researchers have developed a range of innovative solutions, from smartphone-based cough analysis to child-friendly screening systems, designed to overcome longstanding challenges in TB detection—especially in low-resource settings. One breakthrough involves using artificial intelligence to analyze cough sounds captured through a smartphone. The system, trained on thousands of recorded coughs from both TB-positive and healthy individuals, can identify subtle acoustic patterns associated with TB. Early results show the tool can detect TB with high accuracy, even in people who show no visible symptoms. Because smartphones are widely available, this approach could enable mass screening in remote or underserved communities where access to medical facilities is limited. Another innovation focuses on children, who are often difficult to diagnose due to the challenges of collecting sputum samples and the low sensitivity of traditional tests. A new AI-driven screening system uses a simple, non-invasive breath test combined with machine learning algorithms to detect TB biomarkers in exhaled air. The device is designed to be child-friendly, reducing anxiety and improving cooperation during testing—critical factors for accurate diagnosis in younger populations. Additionally, AI models are being deployed to analyze chest X-rays with greater precision than human radiologists in some cases. These tools can flag suspicious patterns indicative of TB, prioritize high-risk cases, and reduce diagnostic delays. When integrated into mobile health platforms, they allow frontline health workers to conduct screenings on-site and receive immediate feedback. Beyond detection, AI is also helping monitor treatment progress and predict outbreaks. By analyzing patient data—including symptoms, treatment adherence, and environmental factors—AI systems can identify individuals at risk of relapse or drug resistance, enabling timely interventions. Some models even forecast TB hotspots by combining health data with socioeconomic and climate information, allowing public health officials to allocate resources more effectively. Experts say these tools could dramatically improve TB outcomes, especially in regions with high disease burden and weak health infrastructure. However, challenges remain, including ensuring data privacy, validating models across diverse populations, and integrating AI into existing healthcare systems. Still, with continued development and investment, these AI innovations hold the potential to transform TB control and bring the world closer to eliminating the disease.