This review article delves into the prevalent global health threats posed by respiratory infections, particularly Tuberculosis (TB) and pneumonia, which significantly impact morbidity and mortality rates worldwide. The paper underscores the crucial role of medical imaging, specifically X-rays, in the detection and diagnosis of these infections, even in resource-limited settings. It addresses various challenges in accurately interpreting X-ray images, including the lack of standardization and the need for high-resolution imaging, and explores how modern digital X-rays can bridge some of these gaps. The article further investigates the burgeoning field of Artificial Intelligence (AI), highlighting its potential to enhance diagnostic accuracy through machine learning and deep learning techniques. By examining different datasets and AI methodologies, this review demonstrates AI's capability to improve the speed and accuracy of diagnosing TB and pneumonia, thereby potentially reducing the global health burden caused by these diseases.