In order to implement market segmentation as a key strategy for promoting the widespread use of emergent agility technologies such as electric vehicles (EVs). The adoption of electric vehicles (EVs) is predicted to rise substantially in the near future due to their low operating costs and minimal emissions. It will therefore greatly increase interest in further academic research in the future. This study aims to combine the approaches of “perceived benefits-attitude-intention” in order to examine and identify many categories of potential EV purchasers based on psychographic, behavioral, and socioeconomic aspects. In this work, different LSTM model variants, GRU, and Bi-LSTM models were utilized. We are taking the electric vehicle population dataset from Kaggle competition. According to the report, three different young groupings for segmenting consumers and markets behavioral, psychographic, and demographic are thought to be forming relationships with purchasers of electric vehicles (EVs). The implications are outlined, which can provide researchers and decision-makers some helpful guidance on how to encourage the use of EVs in light of the expanding sustainable transportation industry. We compared the models with their Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). The Bi-LSTM model gives a MAE score of 0.417, RMSE score of 1.254, and MSE score of 1.573, which is minimal as compared to other models.