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BACKGROUND: Chronic respiratory diseases (CRDs) such as COPD and asthma have a substantial impact on patients and healthcare systems. Recent research on diagnosing and monitoring CRDs highlights the potential of continuous measurement of physiological parameters using nonlinear measures such as entropy analysis. Entropy measures the irregularity and complexity of physiological signals, reflecting the engagement of physiological control mechanisms. This systematic review examines the current evidence on changes in the entropy of physiological signals in CRDs. METHODS: The review follows Preferred Reporting in Systematic Reviews and Meta-Analyses (PRISMA) guidelines and includes studies from databases such as Scopus, Medline, CINAHL and Embase. Quality assessment was conducted using the Newcastle-Ottawa Scale. Evidence was qualitatively synthesised, taking into account entropy signals, entropy type and results. RESULTS: 11 studies met the inclusion criteria. Entropy in signals including heart rate variability (HRV), airflow, peripheral oxygen saturation (S pO2 ), inter-breath interval and tidal volume were evaluated. The findings indicated that patients with COPD and asthma exhibit lower entropy in HRV and airflow compared to healthy controls, with entropy decreasing as disease severity increases. Conversely, S pO2 entropy values were increased during an exacerbation compared to stable COPD. CONCLUSION: The review highlights the potential of entropy analysis of physiological signals for early detection of COPD exacerbations and for differentiating between various levels of disease severity in both COPD and asthma. Additionally, it identifies research gaps, particularly in relation to other CRDs such as bronchiectasis and interstitial lung diseases. Further research is needed to facilitate the development of this approach into a fully effective tool for clinical practice.