To mitigate intermittency from renewable energy sources and present a sustainable alternative to fossil-fuel-based transportation, battery energy storage systems (BESSs) have drawn attention from both academia and industry in the last years. Despite different alternatives, Lithium-ion batteries (LIBs) have become the dominant technology for BESSs and electric vehicles. Therefore, knowledge of lithium-ion battery aging and lifetime estimation is a fundamental aspect for ensuring secure and reliable operations of different systems. This paper presents an analysis of five machine learning models, namely linear regression, k-nearest Neighbors (kNN), random forest (RF), support vector regression (SVR), multi-layer perceptron (MLP), in estimating the state of health (SOH) of LIB cells under different conditions. A total of 12 battery cells, cycled under three different temperatures (, , ) and two discharge C-Rates (1C and 2C), were utilized for validation using mean absolute error (MAE) and R square () coefficient as performance indicators. Results indicated that both kNN and linear regression models achieved the lowest MAE values, with the linear regression model obtaining the highest value. On the contrary, the MLP model showed the worse results among all models tested. A statistical analysis corroborated the results, indicating that the less complex learning models are suitable for estimating the non-linear SOH of LIBs.