The Proposed work evaluates the effectiveness of the Recurrent Neural Network-Gradient Boosting Machine (RNN-GBM) hybrid model for predicting the State of Charge (SoC) in battery management systems. Traditional methods such as Coulomb counting, Kalman filtering, Extended Kalman Filtering (EKF), and Adaptive Kalman Filtering (AKF) are compared against the RNN-GBM hybrid model. The methodology involves a comprehensive analysis of dataset characteristics, model implementation details, and evaluation metrics Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), providing insights into their effectiveness and potential applications. Results indicate that the RNN-GBM hybrid model demonstrates promising performance compared to traditional techniques, offering potential advancements in SoC prediction accuracy and reliability.