Tuberculosis (TB) remains a significant global health challenge, with millions of new infections reported annually. In this review paper, we explore the evolving landscape of TB diagnosis, focusing on the pivotal role played by artificial intelligence (AI) in addressing the complexities associated with this chronic infectious disease. The paper begins with an overview of TB, highlighting its prevalence, transmission, and the emergence of drug-resistant strains, underscoring the urgent need for innovative diagnostic approaches. The review delves into the limitations of conventional diagnostic methods and introduces the potential of computer-aided diagnostics (CAD) tools as a transformative solution. A detailed analysis of recent research efforts showcases various AI-based models that have demonstrated high performance in TB detection. These models employ advanced techniques such as image pre-processing, lung field segmentation, and feature extraction to enhance diagnostic accuracy. The integration of deep learning, fuzzy logic, genetic algorithms, and artificial immune systems into AI models is discussed, showcasing their ability to improve specificity and efficiency in TB diagnosis. Furthermore, the review explores the development of mobile health technologies leveraging deep learning to address diagnosis challenges in marginalized and developing regions. The airborne nature of TB transmission and risk factors associated with active and latent TB cases are outlined to provide context for the importance of early and accurate diagnosis. The paper also discusses the various manifestations of TB, including extra pulmonary tuberculosis (EPT) and military tuberculosis, emphasizing the need for precise diagnostic tools. The classification of TB into multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) is explored, highlighting the impact of irregular treatment and drug supply issues on the emergence of resistant strains. The review concludes with a reflection on the promising advancements in AI-driven diagnostic tools, such as CAD4TB, Lunit INSIGHT, and qXR, and their potential to reshape the landscape of TB diagnosis, offering hope for more effective and timely interventions.