One of the most important areas related to health care in medical literature is lung diseases, common ones are categorized as COPD, Asthma, Lung Cancer, Cystic Fibrosis, Tuberculosis, Pulmonary Edema, Pulmonary Embolism, Pulmonary Hypertension, Pneumonia, Interstitial Lung Disease (ILD), and Covid-19. In this paper, each of these is analyzed by its particular causes, diagnostic indicators, and symptoms. The paper gives a comprehensive overview of these lung diseases, highlighting their various causes and symptoms to enable early detection and treatment. The paper also explains the major areas that are affected in a human body due to lung disorders are the airways, air sacs (Alveoli), Interstitium, Blood Vessels, Pleura, Chest wall. The main objective of this paper is to analyze the detection of different types of lung diseases using ML and compare them with respect to different parameters namely Dataset Selection, Source of Dataset, Findings, Limitations, Future Scope, Disease Predicted, Classification type and Accuracy rate. The comparison for the different accuracy rates of various research papers is also shown in the paper. It clearly indicates that most of the researches have achieved more than 90% of accuracy rates for their work in a global scenario. Looking at the limitations and the future scope it is envisaged that there is a lot of scope for further research in Indian Scenario.