Lithium-ion batteries are widely used due to their high energy density & low self-discharge. Early detection of inadequate performance through Prognostic & Health Management (PHM) can reduce costs & prevent accidents. A data-driven, LSTM-based approach was tested using NASA's battery dataset & found to match or surpass other ML algorithms in predicting battery SoH & SOC.