Amid Prime Minister Narendra Modi’s commitment of ‘TB Mukt Bharat’ by 2025, Artificial intelligence (AI) systems have been introduced by the authorities in Kashmir valley to identify possible cases of tuberculosis in order to improve surveillance. As per media reports, an AI system has been implemented in districts that have achieved tuberculosis-free status in order to maintain increased surveillance and ultimately eradicate the disease. This forward-looking approach is not merely about sustaining progress but actively working towards the complete elimination of the disease. The introduction of AI in tuberculosis surveillance represents a significant development in improving the efficiency and reach of healthcare services. AI algorithms possess the capability to effectively detect potential tuberculosis cases, streamlining the identification process and allowing for more targeted resource allocation. While AI is expected to substantially reduce the number of patients requiring extensive testing, it should be complemented by thorough clinical assessments and additional diagnostic procedures. The combined approach of AI-driven preliminary screening and subsequent clinical evaluations is poised to revolutionise tuberculosis detection and management. By reducing the number of extensive tests needed, this approach not only proves cost-effective but also enhances the overall effectiveness of resource allocation. The financial efficiency achieved through AI-driven screening ensures that limited healthcare resources are utilized optimally, contributing to the broader goal of creating a tuberculosis-free environment. Early detection is paramount in the battle against tuberculosis, a highly contagious disease that poses significant public health challenges. As per reports, AI’s role in screening patients within the community facilitates the early identification of tuberculosis cases in the union territory of Jammu and Kashmir. This, in turn, leads to better treatment outcomes and enables shorter, more targeted treatment courses. The pivotal role of AI in early detection is underscored by its potential to curtail the spread of the disease, especially in densely populated or remote areas where traditional diagnostic facilities may be limited. The integration of AI in tuberculosis diagnosis holds promise not only for Jammu & Kashmir but also for the global fight against the disease. Remote and underserved areas, often lacking sophisticated diagnostic facilities, stand to benefit significantly from these technological advancements. The ability of AI to bridge the gap in healthcare infrastructure, especially in regions with limited resources, is a testament to its potential in transforming healthcare delivery. Also, the synergy between AI and traditional clinical assessments emerges as a powerful tool. While AI expedites the initial screening process, ensuring a more focused approach to subsequent clinical evaluations enhances the accuracy of tuberculosis diagnosis. This combined methodology not only accelerates the identification of cases but also ensures that the health system remains robust, responsive, and adaptable to evolving healthcare challenges. The inclusion of AI in tuberculosis diagnosis in Jammu & Kashmir therefore can be a beacon of hope for regions grappling with limited healthcare resources. This innovative approach aligns with the broader national goal of achieving a tuberculosis-free India. The success of such initiatives not only hinges on technological advancements but also on sustained collaborative efforts and public awareness campaigns.