The application of link prediction models within the framework of comorbidity networks presents considerable promise in the detection of individuals who may be at risk of developing particular diseases. In this paper we have proposed the methodology leverages the identifiable comorbidity patterns present in the network, thereby facilitating precise forecasting of illness onset. The proposed methodology involves the construction of a comorbidity network, where nodes represent illness and linkages represent the simultaneous occurrence of two conditions in a patient. The enhancement of the network’s effectiveness is achieved through the consideration of several factors, encompassing the genetic and molecular information of the patient, their lifestyle choices, demographic attributes, and environmental impacts. The examination of the network can unveil the prospective ailments that an individual may experience in subsequent periods. The utilization of link prediction algorithms in this complex network presents a significant opportunity to forecast the occurrence of diseases in a patient by analyzing the discernible patterns and connections among comorbidities in the network. In this paper, we have discussed the significant potential for predicting future comorbidities using link prediction. We have also elucidated upon a comprehensive methodology for link prediction and discussed its implications in the field of medicine and electronic health care data. However, it is crucial to recognize the need for further research in order to validate these approaches and improve their accuracy.