This paper explores the use of audio-based analysis with machine learning for lung disease recognition. It begins by discussing the need for this research area, providing an overview of the field, and outlining the motivations behind it. The methodology used in the study is detailed, along with a discussion on the elements of sound-based lung disease classification using machine learning algorithms. These elements include commonly used datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study also reviews existing literature on lung screening, including a summary table of references and discusses the literature gaps in the existing studies. The paper concludes that sound-based machine learning in the classification of respiratory diseases has promising results, but large-scale investigations are needed to solidify these findings and foster wider adoption within the medical community.This paper explores the use of audio-based analysis with machine learning for lung disease recognition. It begins by discussing the need for this research area, providing an overview of the field, and outlining the motivations behind it. The methodology used in the study is detailed, along with a discussion on the elements of sound-based lung disease classification using machine learning algorithms. These elements include commonly used datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study also reviews existing literature on lung screening, including a summary table of references and discusses the literature gaps in the existing studies. The paper concludes that sound-based machine learning in the classification of respiratory diseases has promising results, but large-scale investigations are needed to solidify these findings and foster wider adoption within the medical community.