<![CDATA[Background: Tuberculosis (TB) remains a significant global health challenge, particularly in countries with poor air quality and high population density. Delayed diagnosis and environmental factors, such as air pollution, contribute to the high prevalence and mortality rates associated with this disease despite advancements in treatment and prevention. A review of the literature highlights a significant association between long-term exposure to air pollutants, such as delicate particulate matter ( ) and an increased risk of TB. Internet of Things (IoT) technology, which integrates real-time environmental sensors with analytical algorithms, offers the potential to support TB prevention through data-driven and modern technological approaches. This study aims to design a conceptual framework based on IoT technology to enhance early TB detection through air quality monitoring. Methods: A literature review was conducted from 2020 to 2025, focusing on designing the Safe Breath conceptual framework. Relevant articles were retrieved from databases including PubMed, ScienceDirect, and Google Scholar, filtered by inclusion criteria and full-text availability. Data were synthesized to explore the relationship between air quality and TB incidence. Findings Poor air quality is closely linked to TB risk, making environmental monitoring essential in disease control. IoT technology can collect real-time data through air quality sensors, monitoring environmental risk factors continuously. The Safe Breath application concept integrates air sensors with early detection features to improve TB screening accuracy while encouraging community participation in disease prevention efforts. Conclusion: The proposed Safe Breath application combines IoT technology with air quality monitoring and early detection systems, improving screening accuracy and proactive TB control through a community-based approach. Novelty/Originality of this article: This study presents a novel approach by integrating IoT technology and environmental monitoring for TB control. The combined use of air sensors and early detection tools offers a scalable, data-driven solution for global TB prevention.]]>
