High-frequency resistance (HFR) is a critical quantity strongly related to a fuel cell system's performance. As such, an accurate and timely prediction of HFR is useful for understanding the system's operating status and the corresponding control strategy optimization. It is beneficial to estimate the fuel cell system's HFR from the measurable operating conditions without resorting to costly HFR measurement devices, the latter of which are difficult to implement at the real automotive scale. In this study, we propose a data-driven approach for a real-time prediction of HFR. Specifically, we use a long short-term memory (LSTM) based machine learning model that takes into account both the current and past states of the fuel cell, as characterized through a set of sensors. These sensor signals form the input to the LSTM. The data is experimentally collected from a vehicle lab that operates a 100 kW automotive fuel cell stack running on a automotive-scale test station. Our current results indicate that our prediction model achieves high accuracy HFR predictions and outperforms other frequently used regression models. We also study the effect of the extracted features generated by our LSTM model. Our study finds that even a simple LSTM based model can accurately predict HFR values.