The rapid evolution of AI and ML technologies is transforming numerous fields, healthcare included, since applications, particularly in pulmonary disease diagnostics, have improved implications including greater precision, wider scope or reach, and enhanced efficiency across detection and treatment processes. The focus of the study is on the relationship between the integration of AI & ML with different pulmonary disease diagnosis methods with a key focus on its efficiency relative to other such methods. Primary evidence shows that artificial intelligence-fueled algorithms, especially ones with embedded learning, can yield accuracy exceeding 90% in diagnosing pneumonia and lung cancer. Concerning deep learning applications, brushes with AI tools result in quicker and more accurate diagnostics (p < 0.01). The adoption of AI & ML technologies for pulmonary diagnosis calls for a change in how respiratory disorders are currently managed. There are risks of loss of data and exposure which need to be mitigated through trials in sub-populations before mainstreaming use of these technologies. Mobile computing, digital health, and artificial intelligence technologies are rapidly making new advancements in the field of pulmonary medicine. This research expands our understanding of the significance of AI & ML in somatic practice including but not limited to improving the diagnosis of pulmonary diseases for enhancing care of patients.