Chronic obstructive pulmonary disease (COPD) has been established as a systemic disorder affecting multiple organ systems, yet its impact on renal function remains frequently underrecognized. A well-documented bidirectional crosstalk exists between the lungs and kidneys. In COPD, chronic inflammation serves as the central pathogenic driver: proinflammatory mediators-including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6)-enter systemic circulation, directly impair renal tissue, activate the NF-κB signaling pathway, and trigger oxidative stress responses. These processes promote renal tubular apoptosis and interstitial fibrosis. Furthermore, persistent hypoxemia and hypercapnia activate endogenous hypoxia-inducible factors, leading to renal vasoconstriction and severe metabolic acidosis, thereby establishing a self-perpetuating cycle of progressive kidney injury. Cigarette smoking exerts dual organ toxicity: beyond its well-known pulmonary effects, it induces direct nephrotoxicity via endothelial dysfunction, oxidative stress, and accumulation of heavy metals such as cadmium. Additionally, therapeutic interventions-including pharmacologic agents and mechanical ventilation-may further elevate the risk of acute r chronic kidney injury in COPD patients. Conventional renal biomarkers, such as serum creatinine and blood urea nitrogen, exhibit limited sensitivity and specificity for early detection of kidney damage, often failing to identify subclinical injury until significant functional decline has occurred. In contrast, novel biomarkers offer superior diagnostic performance: kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), and urinary liver-type fatty acid-binding protein (L-FABP) provide rapid, sensitive indicators of proximal tubular injury, whereas cystatin C (CysC) and β-microglobulin (β-MG) reliably reflect subtle alterations in glomerular filtration rate. Moreover, the leukocyte-related inflammatory index model-constructed using readily accessible peripheral blood inflammatory markers-is simple, practical, and highly suitable for clinical implementation. Furthermore, a machine learning model built upon routinely available clinical parameters demonstrates excellent predictive performance for acute kidney injury in COPD patients, achieving an area under the receiver operating characteristic curve of 0.91. Therefore, systematically elucidating the bidirectional crosstalk mechanisms between the lung and kidney, identifying novel biomarkers, and developing associated predictive models-particularly AI-driven early warning systems-hold significant clinical value for the early screening, precise intervention, and improved prognosis of COPD-associated renal injury.