Various machine learning algorithms have been used to estimate state-of-charge (SOC) of calendar-aged lithium-ion pouch cells. Calendar life data was generated by applying galvanostatic charge/discharge cycle loads at different storage temperature (35°C and 60°C) and conditions (fully-discharged and fully-charged). The data was obtained at various C-rates for duration of 10 months at one-month intervals. The wininng model, Random Forest (RF), has achieved a R2 score of 99.98% and a mean absolute error (MAE) of 0.14% over test data, confirming the ability of RF to capture input-output dependency. The model will be employed to estimate the SOC of calendar-aged lithium-ion batteries which is essential for the reliable operation of electric vehicles (EVs).