In the healthcare sector, various specialized fields focus on predicting chronic and infectious diseases, diagnosing conditions, and monitoring patients in real time. Advanced algorithms, training datasets, and machine learning models are increasingly applied across these areas. Disease prediction based on observed symptoms becomes more effective when integrated with substantial datasets and machine learning techniques. The datasets used for training typically include common symptoms that individuals might experience, enhancing the accuracy and relevance of disease predictions. The primary objective is to offer a comprehensive overview of machine learning methods applied to respiratory disease datasets. This analysis reviews relevant literature, identifies existing gaps, and helps researchers prepare for applying machine learning techniques to respiratory diseases.