The lithium-ion battery technology has led to significant changes in the usage of rechargeable batteries due to its low discharge current, high energy capacity, and long charge/discharge cycles. The easy production of portable and high-energy density batteries has not only contributed to the proliferation of smart devices like the Internet of Things (IoT) devices but has also led to an increase in the use of electric vehicles (EVs). As battery chemistry varies based on manufacturers and storage conditions, the importance of determining the charge lifespan and capacity of batteries connected to smart devices is growing progressively. Therefore, various studies are being conducted to assess capacity and lifespan calculations for Li-Ion batteries. In this study, the behavioral patterns of Li-Ion cells in end-user products are analyzed, aiming to predict capacities for similar battery groups. For this purpose, besides a fundamental linear regression analysis, regression analysis using Gaussian Process Regression (GPR) and Convolutional Neural Networks (CNN) is carried out. The regression performance is evaluated using diverse metric criteria such as R-squared (R2), Adjusted R-squared (Adj. R2), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Normalized Mean Squared Error (NMSE).