The package Urgent Computing Integrated Services for EarthQuakes (UCIS4EQ) is a workflow developed in the framework of the Center of Excellence for Exascale in Solid Earth (ChEESE). Inside this complex system, an important task of UCIS4EQ involves predicting the damage potential of a new earthquake in order to provide information for the decision-maker to habilitate the protocol, guidelines, and the computing resources to run an urgent job in HPC centers. In this work, we present a method that classifies the damage potential of an earthquake by using machine learning algorithms. Our method requires the information of the estimates of peak ground accelerations (PGA, at 90% non-exceedance for 50 years); socio-economic characteristics of the affected countries; and the historical Significant Earthquake Database. As a target, we use the modified Mercalli intensity (MMI) scale. We consider the PGA estimates, latitude, longitude, depth, magnitude, infrastructure index and infrastructure quality as features. As classifier algorithms, we apply the Random Forest, Support Vector Machine, and XGBoost. Our result is a metamodel that uses a voting system to classify an event as urgent or non-urgent. Our results indicate an accuracy of approximately 70 percent. We consider such accuracy satisfactory given the short training time, the novelty of the methodology and the database quality. The code developed in this work will provide a microservice in UCIS4EQ, and thus, it will assist in the urgent seismic simulation developed in ChEESE.