<p>In recent years, using “Electric Vehicles (EVs)” in an automobile zone has reduced the impact of air pollution and greenhouse gas emissions on environmental systems. Efficient charge management of EVs plays a significant role in improving vehicle safety, maximizing battery life and minimizing cost, which impacts the energy consumption ranges of EVs in the transportation system. Further, the batteries, along with the strong non-linear characteristics as well as time-variables, have been impacted by the random factors like operational conditions, and driving loads in the applications of EVs, and therefore, it is significant to tackle the limitations of the conventional “State of Charge (SoC)” assessment methods. This approach developed an innovative neural network learning methodology to solve an existing issue. In a suggested EV and its SoC estimation technique, essential temperature, available and requested battery thermal factor, current, actual power loss, air cooling temperature data, and power in a training process are aggregated through 10 different drive cycles. The acquired data is then provided for the feature extraction process. The conventional feature extraction models, like the Principal Component Analysis (PCA), have slow convergence in the training process, and it becomes complex for balancing feature selection and extraction performance, leading to overfitting issues. Thus, the developed method used the Restricted Boltzmann Machines (RBM)-based feature extraction mechanism to learn and capture high-level features from raw data in an efficient manner, and the extracted features are given to the charge estimation phase. The conventional Residual Long Short Term Memory (ResLSTM) approach does not enable safe and efficient discharging and charging performance, it reduces the lifetime of the battery, and is not capable of predicting the remaining range of energy in the EV. Therefore, the adaptive and attention mechanisms are incorporated with a ResLSTM in the developed work to produce the novel approach named Adaptive Residual Long Short Term Memory with Attention Mechanism (A-ResLSTM-AM) to significantly optimize the power consumption range and enhance the charge estimation process, which improves the battery life by avoiding overcharging and over-discharging. To further enhance the performance, the Hybrid Position of Tasmanian Devil and Black Widow (HP-TDBW) is used in the A-ResLSTM-AM for tuning the parameters. The traditional Tasmanian Devil and Black Widow (TDBW) struggles to quickly converge to the optimal solution in complex problems and cannot manage large datasets in a limited duration, to reduces the generalization ability. Unlike TDBW, the proposed HP-TDBW provides a novel contribution to efficiently tune the parameters of A-ResLSTM-AM. This optimization process can lead to reduced fuel consumption and energy losses during transmission and distribution. Hence, the implemented “EV SoC estimation technique” secures a superior presentation rate over other existing techniques in different experimental observations. It attains 34 in MEP, 0.25 in SMAPE, 114 in MASE, 5.2 in MAE, and 5.8 in RMSE measures in dataset 1. Also, it achieves 31 in MEP, 0.36 in SMAPE, 118 in MASE, 4 in MAE, and 5.1 in RMSE in terms of Dataset 2 validation. The proposed Adaptive Residual LSTM with Attention improved SoC estimation (RMSE reduced from 5.6% to 4.4%) compared to baseline LSTM across three real-world drive cycles (UDDS, WLTC, and HWFET). These improvements directly contribute to more accurate and reliable energy management systems in EVs.</p>