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Lithium-ion batteries (LIBs) have become a focus of research interest for electric vehicles (EVs) due to their high volumetric and gravimetric energy storage capability, lower self-discharge rate, and excellent rechargeability coupled with high operational voltage as compared with the lead-acid batteries. This paper presents different machine learning approaches to predict health indicators & usable cycle life of LIBs. Here, we focus on two important battery health indicators i.e., battery discharge capacity and Internal resistance (IR). We used publicly available multi-cycled data of the Lithium Iron Phosphate (LFP), Lithium-Nickel-Manganese-Cobalt-Oxide (NMC) and Lithium Cobalt Oxide (LCO) cells. The approach proposed for predicting health indicators involves using a time-series model in the areas where the actual data i.e., from the Beginning of life (BOL) to the End of life (EOL) is not available.