<p>Chronic Obstructive Pulmonary Disease (COPD) is a significant worldwide health burden, diagnosed late in life because of dependence on spirometry and clinical judgment. Artificial Intelligence (AI) has revolutionary potential in early COPD diagnosis using computational biomarkers and bioacoustic intelligence. This review discusses AI-based methods using pulmonary function parameters, vital signs, biochemical markers, and respiratory sounds for non-invasive, computer-aided diagnosis. Machine learning and deep learning architectures such as CNNs, LSTMs, and ensemble learning have been highly accurate in detection of disease-specific acoustic and physiological patterns. Despite significant progress, several main challenges remain such as dataset standardization, model generalizability across populations, regulatory barriers, and low clinician confidence in AI-driven decisions. Emerging trends are federated learning for training multi-hospital AI models, AI-powered wearable diagnostics, and explainable AI (XAI) to fuel greater clinical uptake. AI-driven diagnosis of COPD has the potential to improve early detection, enable personalized treatment, and aid scalable healthcare solutions, particularly in resource-constrained settings.</p>