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Vehicles that run on petrol face competition from electric vehicles (EVs), which are more environmentally friendly and consume less energy than gasoline-powered automobiles. If we can predict the states that have an effect on charging, we might be able to estimate how much charging electric vehicle owners will require in the future. It is also capable of operating and managing charging infrastructure, in addition to providing users with individualised charge capacity statistics based on where they are precisely at the moment. As a result of this, developing a reliable model that can accurately predict the charging state of an electric vehicle has become an important issue. Based on the findings of this study, it is recommended to employ a combination of machine learning and deep learning in order to guarantee that the charging process is both secure and dependable, and that the battery does not become overcharged or over-drained. It has been suggested that a process of feature extraction using Recursive Neural Networks (RNNs) be utilised in order to obtain sufficient feature information regarding the battery. The bidirectional gated recurrent unit framework (GRU) was then established in the research project in order to make an educated guess as to the state of the electric vehicle. It is because of the information that the GRU obtains from the output of the RNNs that the model is significantly more useful. As a result of its more straightforward structure, the RNN-GRU is less effective when it comes to computing. In light of the findings of the tests, it is clear that the GRU method is capable of accurately monitoring the mileage of an electric vehicle. Based on the results of numerous tests conducted in the real world, it has been demonstrated that a mixed deep learning-based prediction method has the potential to provide a faster convergence speed and a lower error rate than the conventional method of obtaining an estimate of the state of charge.