The proposed study evaluates the critical role of deep learning (DL) models in predicting function decline in patients with Chronic Obstructive Pulmonary Disease (COPD). The crucial need originates from the limitations of traditional methods, which frequently depend on static models that are incapable of adjusting to the dynamic nature of COPD progression. These outdated ways of anticipating decline patterns lack precision, limiting early intervention and personalized patient care. In this work, better accuracy is obtained for the proposed DL methods that consider not only various biomarkers but also particular details of the patients. Computational efficiency and measure of various performance metrics show the potential of the proposed method over traditional methods. Hence, it can be concluded that the proposed DL algorithm represents a tremendous mark in anticipating renal function decline in COPD patients.