Tuberculosis (TB) continues to pose a major public health challenge in India, where diagnostic delays, fragmented laboratory capacity, workforce shortages, and infrastructural gaps hinder timely detection and treatment. While conventional tools such as sputum microscopy, culture, GeneXpert, and line-probe assays remain central to TB control, their implementation is often constrained by resource limitations and variability in field performance. Against this backdrop, artificial intelligence (AI) has emerged as a transformative force capable of strengthening TB services across the entire care cascade. AI-driven innovations now support digital microscopy, enhance culture growth detection, improve interpretation of CBNAAT and LPA outputs, and facilitate early identification of drug resistance. In radiology, computer-aided detection tools provide rapid, consistent interpretation of chest X-rays, enabling efficient triage even in settings without radiologists. Cough sound analysis and multimodal algorithms offer new pathways for noninvasive TB screening and prioritization. AI-enhanced clinical decision support systems (CDSS) help clinicians adhere to national guidelines, manage complex drug regimens, monitor toxicity, and track follow-up schedules. At the programmatic level, machine-learning models enable real-time surveillance, epidemiological forecasting, hotspot identification, supply-chain optimization, and evaluation of intervention impact—supporting data-driven decision-making under the National TB Elimination Programme. Furthermore, AI-assisted pharmacovigilance systems detect early signals of adverse drug reactions, while predictive models monitor adherence and identify patients at risk of treatment interruption. Despite these advances, challenges remain regarding algorithmic bias, data privacy, local validation needs, infrastructural gaps, and ethical–legal considerations. AI must function as an aid—not a replacement—to human expertise. When deployed responsibly, with strong governance and contextual adaptation, AI can significantly enhance diagnostic accuracy, accelerate treatment initiation, and strengthen surveillance, ultimately contributing to India’s goal of eliminating TB through a more efficient, equitable, and patient-centered care system.