This research article proposes a novel method for predicting the discharge capabilities of lithium-ion (Li-ion) batteries using a digital twin (DT) model. The study combines advanced machine learning techniques, such as AdaBoost and long short-term memory (LSTM) networks, with a semiempirical mathematical structure to create a virtual representation that mimics the behavior of actual batteries in real time. Various metaheuristic optimization methods, including antlion, grey wolf optimization (GWO), and improved grey wolf optimization (IGWO), are used to adjust hyperparameters and optimize the models. The performance of the models is evaluated using mean absolute error (MAE) and root-mean-square error (RMSE) after extensive training and ten-fold cross-validation. The IGWO-AdaBoost DT model stands out as the most accurate performer, achieving the lowest MAE and RMSE values of 0.01. The findings offer valuable insights into the potential of digital twin models for accurately predicting battery discharge capacity, contributing to the development of more robust and efficient battery systems. The research has significant implications for the energy storage industry, particularly in optimizing battery design, reducing costs, and improving sustainability.This research article proposes a novel method for predicting the discharge capabilities of lithium-ion (Li-ion) batteries using a digital twin (DT) model. The study combines advanced machine learning techniques, such as AdaBoost and long short-term memory (LSTM) networks, with a semiempirical mathematical structure to create a virtual representation that mimics the behavior of actual batteries in real time. Various metaheuristic optimization methods, including antlion, grey wolf optimization (GWO), and improved grey wolf optimization (IGWO), are used to adjust hyperparameters and optimize the models. The performance of the models is evaluated using mean absolute error (MAE) and root-mean-square error (RMSE) after extensive training and ten-fold cross-validation. The IGWO-AdaBoost DT model stands out as the most accurate performer, achieving the lowest MAE and RMSE values of 0.01. The findings offer valuable insights into the potential of digital twin models for accurately predicting battery discharge capacity, contributing to the development of more robust and efficient battery systems. The research has significant implications for the energy storage industry, particularly in optimizing battery design, reducing costs, and improving sustainability.