Lithium-ion batteries’ state-of-charge prediction (SoC) cannot be directly measured due to their chemical structure. Therefore, a prediction can be made using the measurable data of the battery. The limited measurable data (current, voltage, and temperature) and the small changes in charge/discharge curves over time further complicate the prediction process. Recurrent neural network-based deep learning algorithms, capable of making predictions with a small number of input data, have become widely used in this field. Particularly, the use of Long Short-Term Memory (LSTM) has shown successful results in one-dimensional and slowly changing data over time. However, these approaches require high computational power for training and testing processes. The window length of the data used as input is one of the major factors affecting the prediction time. The window length of the data varies depending on the sampling frequency and the length of the lookback period. Reducing the window length to shorten, the prediction time makes feature extraction from the data difficult. In this case, adjusting the sampling frequency and window length properly will improve the prediction accuracy and time. Therefore, this study presents the effects of sampling frequency and window length on the prediction accuracy for LSTM-based deep learning approaches. Prediction results were examined using different metrics such as MAE, MSE, training, and testing time. The study’s results indicate that training and testing times can be shortened when the sampling frequency and window length are properly adjusted.