<p>Respiratory disorders, a leading global health issue, are often inadequately diagnosed in both developing and developed countries despite the availability of effective treatments. While lung sounds have been widely studied for early diagnosis, speech parameter-based analysis. remains underexplored. This review examines the potential of speech analysis for detecting respiratory disorders, focusing on the challenges and existing methods. We analyze studies that employ speech parameters in respiratory disease detection, comparing them with traditional methods such as lung sound analysis and medical imaging. Our review identifies a significant gap in the use of speech-based diagnostics for respiratory conditions, with most studies relying on lung sounds and imaging. The few speech-based studies highlight the potential for speech to serve as an early indicator, though the lack of open-source speech databases for various disorders limits progress. We analyzed peer-reviewed studies published between 2010 and 2024, identifying key trends in feature extraction methods, speech datasets, and machine learning techniques. Our review reveals that only 17% of the studies utilized publicly available speech datasets, and deep learning methods were employed in fewer than 25% of cases. These findings reveal a significant research gap in standardized datasets and advanced modeling approaches. This article is the first comprehensive review of speech-based respiratory disorder assessment, underscoring the need for deep learning approaches and publicly available speech datasets to advance the field. For effective early diagnosis and treatment, future research should focus on integrating speech analysis with existing diagnostic methods, leveraging machine learning and deep learning techniques for better accuracy.</p>