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In Chile and the world, the supply of medical hours to provide care has been reduced due to the health crisis caused by COVID-19. As of December 2021, the outlook has been critical in Chile, both in medical and surgical care, where 1.7 million people wait for care, and the wait for surgery has risen from 348 to 525 days on average. This occurs mainly when the demand for care exceeds the supply available in the public system, which has caused serious problems in patients who will remain on hold and health teams have implemented management measures through prioritization measures so that patients are treated on time. In this paper, we propose a methodology to work in net for predicting the prioritization of patients on surgical waiting lists (SWL) embodied with a machine learning scheme for a high complexity hospital (HCH) in Chile. That is linked to the risk of each waiting patient. The work presents the following contributions; The first contribution is a network method that predicts the priority order of anonymous patients entering the SWL. The second contribution is a dynamic quantification of the risk of waiting patients. The third contribution is a patient selection protocol based on a dynamic update of the SWL based on the components of prioritization, risk, and clinical criteria. The optimization of the process was measured by a simulation of the total times of the system in HCH. The prioritization strategy proposed savings of medical hours allowing 20% additional surgeries to be performed, thus reducing SWL by 10%. The risk of waiting patients could drop by up to 8% annually. We hope to implement this methodology in real health care units. [ABSTRACT FROM AUTHOR]