This study presents an innovative methodology to improve the maintenance of railway infrastructures, integrating degradation models and economic optimization. The combination of a degradation model and a semi-Markovian model supported by the Weibull function, calculates the economically optimal degradation, to release the preventive intervention. The development of digital twins of assets and infrastructures makes possible the integration of online data and the incorporation of modelsthat help the maintenance manager in decision-making. Organizations must determine the information to be collected from the assets. Transform the information into inputs for the models and, after their application, would allow them to identify the most economically favorable global maintenance scenarios. This work evolves the decision support on preventive maintenance implementation in the railway sector. From a decision table, used by railway managers, with three levels of risk established subjectively on an experimental basis, it evolves to the objective methodology, designed to determine the optimal degradation. The methodology can be applied to assets by maintenance managers but can also be incorporated into the digital twins. The case study illustrates the practical application of this methodology to the degradation ofrailway track sections. The research emphasizes the importance of data-driven decision-making for economic maintenance management. However, other priorities such as transportation safety and efficiency could complement the economic one.