This paper focuses on predicting lithium battery capacity and state of health based on pre-recorded datasets. Data-driven techniques, including machine learning approaches have become prevalent. However, data is often inputted into these methods without much thought. Therefore, data pre-processing before utilizing machine learning techniques can optimize the performance and efficiency of these models. Since the battery degradation process is highly nonlinear and influenced by multiple factors, including both manufacturing aspects and operating conditions, which complicate the battery capacity prognostics, we need some tool that can keep track on useful data and produce reliable predictions in the end . Specifically, the main methodology of this study includes defining indirect health indicators as inputs into two machine learning techniques: Gaussian Process Regression (GPR) and Feed Forward Neural Network. To demonstrate this method, the NASA battery dataset  is utilized; in particular, the indirect health indicators are extracted from the battery's temperature and charging state (the voltage and current change process), then a machine learning model is utilized to optimize prediction of capacity degradation.The first method for predicting the State of Health (SOH) of a battery is Gaussian Process Regression, which modifies the isotropic squared exponential kernel with an automatic relevance determination structure. The entire process extracts the highly relevant input features for capacity predictions . Utilizing this method enables quick extraction of relevant data and provides fast predictions of capacity.The second strategy is Feedforward Neural Networking (FFNN). Overall, FFNN is a widely used machine learning method with outstanding performance in nonlinear modeling. It first intake one input as the starting layer, and each layer except the output layer is fully connected to the next layer, so the final prediction of FFNN is tightly connected with the previous data .However, the inputs to these advanced models need to be considered, as irrelevant inputs can lead to inaccurate predictions (see figure 1). Therefore, indirect health indicator (IHI) is applied as a form of feature extraction as an input to the model. Indirect health indicator is what affects the capacity of a battery during the charging or discharging state. During those processes, the performance of lithium-ion batteries will deteriorate with capacity decreasing and impedance increasing, which will cause equipment and system failures or even catastrophic loss . Therefore, it is necessary to consider the IHI before predicting the model.For this study, indirect health indicators are determined with data pre-processing and are subsequently inputted into the GPR and FFNN. The results show success for capacity estimation for lithium ion batteries.