Micro direct methanol fuel cell (μDMFC) has the advantages of high energy density, portable use, fast replenishment, and eco-friendliness. However, the practical service life of μDMFC is often limited due to the deterioration of membrane electrode assembly in electrochemical reaction. Therefore, it is necessary to evaluate the health status and remaining useful life (RUL) of the cell to provide decision-making support for fuel cell characteristic modification and control strategy. Considering the pros and cons of data-driven and model-based methods, an RUL prediction method for μDMFC based on equivalent circuit model (ECM) is proposed. Among the degradation indicators of μDMFC, the cell output voltage can be monitored in real time to obtain the degradation trend. However, this indicator cannot provide accurate prediction results alone under dynamic operating conditions. Deeper-level information, such as the internal impedance, can be obtained by investigating the electrochemical impedance spectroscopy (EIS), but such in-depth information is difficult to be monitored in real time and can only be measured offline at low frequencies. Moreover, fuel cells are usually under dynamic operating conditions in practical applications, so their degradation and service life are affected by the operating conditions. Traditional output voltage regression-based prediction methods cannot cope with dynamic changes in operation. Therefore, the prediction model can be built through scheduled offline measurement of internal degradation indicators. The experimental results show that, compared with the traditional data-driven method, the prediction method based on the internal EIS characterization can better adapt to the variable operating conditions and has a superior performance in RUL predictions.