This review article discusses the use of audio-based analysis with machine learning for lung disease recognition. It highlights the importance of lung sound analysis in diagnosing respiratory conditions, as lung sounds provide valuable information about lung function and can indicate various diseases. Traditional auscultation methods, while useful, are limited by subjectivity, variability, and the inability to capture detailed sound patterns. The article reviews existing research on lung sound classification using machine learning algorithms, including feature extraction techniques, preprocessing methods, artifact removal, and deep learning models. It also discusses the challenges in lung sound analysis, such as noise, imbalanced datasets, and the need for efficient feature extraction. The study emphasizes the potential of deep learning in improving the accuracy and efficiency of lung disease diagnosis. It also highlights the importance of integrating deep learning with lung auscultation screening to enhance diagnostic capabilities. The review concludes that while there is significant potential in using machine learning for lung disease recognition, further research is needed to address existing gaps and improve the reliability and effectiveness of these methods. The study provides a comprehensive overview of current research, identifies key challenges, and suggests future directions for research in this field.This review article discusses the use of audio-based analysis with machine learning for lung disease recognition. It highlights the importance of lung sound analysis in diagnosing respiratory conditions, as lung sounds provide valuable information about lung function and can indicate various diseases. Traditional auscultation methods, while useful, are limited by subjectivity, variability, and the inability to capture detailed sound patterns. The article reviews existing research on lung sound classification using machine learning algorithms, including feature extraction techniques, preprocessing methods, artifact removal, and deep learning models. It also discusses the challenges in lung sound analysis, such as noise, imbalanced datasets, and the need for efficient feature extraction. The study emphasizes the potential of deep learning in improving the accuracy and efficiency of lung disease diagnosis. It also highlights the importance of integrating deep learning with lung auscultation screening to enhance diagnostic capabilities. The review concludes that while there is significant potential in using machine learning for lung disease recognition, further research is needed to address existing gaps and improve the reliability and effectiveness of these methods. The study provides a comprehensive overview of current research, identifies key challenges, and suggests future directions for research in this field.