Accurate estimation of battery state including state of charge (SoC) and state of health (SoH) are crucial for ensuring safety in energy storage applications. The SOC and SOH estimators were independently trained using the same input vector but with different objective functions, no integration between SOC and SOH estimations were explored. In this paper, a unified algorithm, for identifying both SoC and SoH states, is introduced by considering the Bayesian optimization for hyperparameter tuning. This approach allows seamless transition between SoC and SoH estimation without needing separate models for each task. In addition, equipping the dual estimation framework with a unified algorithm for identifying both states would impact the algorithm’s complexity. The suggested BiLSTM model reduces complexity in real-time Battery Management System (BMS) applications by eliminating the need for a separate model to estimate SoH. When compared to other machine learning and deep learning models such as Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), Radial Basis Function Neural Networks (RBF-NN), Recurrent Neural Networks (RNN), and LSTM, the suggested BiLSTM method demonstrates the highest efficiency. Finally, to verify the proposed method’s effectiveness, a comparison among the different evaluation metrics was conducted. The proposed BiLSTM model achieved an average MAE (Mean Absolute Error) of 0.08 and NRMSE (Normalized Root Mean Squared Error) of 0.15 for SoC estimation across various temperatures (5°C, 15°C, 35°C, and 45°C), and an MAE of 3.12 and NRMSE of 0.23 for SoH estimation with a degradation rate of 47% of the cell estimated from the predicted capacity values.