Death rates across the globe are often linked to respiratory illnesses, with severe conditions like- chronic obstructive pulmonary disease (COPD) and asthma being the primary culprits. Early detection of the-se diseases in their initial stages is more crucial than we may realize. The ancient diagnostic technique of lung auscultation, where a stethoscope is placed on the lungs, is renownedbut also has inherent limitations and susceptibility to data distortion due to environmental variables. This led to the deve-lopment of modern solutions, born out of necessity, to address these challenges innovativemethods that harness the power of deep learning algorithms to capture respiratory sounds more accurately. TheInternational Conference on Biomedical and Health Informatics(ICBHI)dataset, containing lung sound recordings, is available to the machine learning community for research and development.Leveraging machine learning and deep learning techniques, with the latter being a subset of machine learning, such as convolutional neural networks, has enabled more accurate diagnoses compared to traditional auscultation methods. These advanced algorithms have achieved impre-ssive voice classification accuracy rates, outperforming conventional approaches. The fusion ofcutting-edgetechnology and medical expertise has the potential to revolutionize respiratory disease detection and management.Scientific investigations and research have demonstrated that when utilizing)ICBHI 2017(data set, its precision varies from 42% to 90%.The goal of this article is to review articles related to the use of deep learning algorithms, which are combined in some articles with other machine learning algorithms, and the way they deal with the ICBHI 2017 dataset.