This research attempts to provide an innovative approach to measuring Catastrophic Health Expenditure (CHE) that captures the dynamics of household assets globally. CHE is a major cause that pushes households into poverty or forces households already in poverty into even deeper poverty. The study estimates how dynamically measured CHE affects a household’s assets by exploiting a panel dataset with a plethora of household financial information. The innovative approach is then validated by comparing the accuracies of future CHE incidence prediction, using the CHE indicators in the current period along with other household characteristics, fitted into a machine-learning classification algorithm.