A major obstacle in medical diagnostics is the categorization of lung disorders, which have hitherto depended on image-based techniques like CT scans and X-rays. The use of medical data-driven methods (such as biomarkers and patient histories) and current developments in sound analysis (such as auscultation) have opened new avenues for more precise and multimodal approaches. One of the current issues is that single-modal approaches have their limitations. For example, image analysis has limited specificity, and non-imaging data is underutilized. Novel approaches that integrate multiple data types—sound, images, and medical records—are the subject of this review study, which seeks to offer a thorough assessment of current trends in lung disease classification. The goals are to recognize important developments, point out the advantages and disadvantages of existing methods, and suggest avenues for further study that can improve diagnostic precision and patient outcomes.