Abstract: Capacity is a crucial metric for evaluating the degradation of lithium-ion batteries (LIBs), playing a vital role in their management and application throughout their lifespan. Various methods for capacity estimation have been developed, including the traditional Ampere-hour integral method, model-driven methods based on equivalent circuit models or electrochemical models, and data-driven methods based on features extracted from partial charging, discharging, or relaxing processes. Current research focuses on improving the accuracy, acquisition speed, and robustness of these capacity estimation methods. This study proposes a rapid and precise method for capacity estimation in LIBs, using electrochemical impedance spectroscopy (EIS) and the extreme gradient boosting machine learning framework. The proposed method concurrently considers the impacts of the state of charge (SOC) and temperature. The model demonstrates the ability to automatically compensate for variations in SOC and temperature, leveraging specific impedance features, provided the input EIS data's SOC and temperature range is encompassed within the training set. Two implementations of the method are presented. The first utilizing EIS features, while the second employs features derived from the distribution of relaxation times. The latter exhibits enhanced adaptability to small datasets. When applied to the complete dataset of this study, the proposed method achieves an R2 value exceeding 0.97 and a mean absolute percentage error below 0.8%.