Chronic obstructive pulmonary disease (COPD) and asthma are among the most serious health care problems across the world. The introduction of artificial intelligence (AI) in medical diagnosis affords an opportunity of elevating the level management of respiratory disease. This review paper discusses the addition of AI-based diagnostic methods to spirometry in the management of asthma and COPD. We analyze the herculean and recent strides made in the field of machine learning techniques, deep learning architectures and their relevance in interpreting the results of spirometry. The Review explores Healthcare AI’s role in assessing medical diagnosis for timely intervention, preventive healthcare, and potential causes of such clinical conditions in patients with these chronic lung diseases. We also examine the benefits but more especially the drawbacks in the uptake of the AI diagnostic tools in practice including availability of data quality, interpretability of the models built and clinical workability. This review integrates existing literature on this subject while laying the strategies that future studies should address in order to provide an in depth understanding if the possible impact of artificial intelligence will have in the diagnostics based on spirometry in asthma and COPD, for the benefit of the patients and reshaping the respiratory medicine.