A bidirectional long short-term memory (Bi-LSTM) model is developed to predict a multi-faceted bearing health characteristic using time series vibration measurements from the Case Western Reserve University (CWRU) seeded fault test data. A maximal amount of data is applied with minimal preprocessing to test the model’s capabilities and several hyperparameters are tuned to maximize model performance. The utility of the CWRU dataset for related problems and the applicability of Bi-LSTM architecture for time series data is highlighted. The proposed model achieved a final test prediction accuracy of 98.42% and had low computation time, making it an interesting candidate for application in bearing fault prognosis.