Morocco, along with numerous other nations, is exploring the potential of electric vehicles (EVs) to reduce dependence on fossil fuels and mitigate the impacts of cli-mate change. This study undertakes the prediction of energy demand in the transport sector by 2050 utilizing artificial intelligence algorithms. Various scenarios regarding the integration of electric vehicles and their impacts on energy demand and CO2 emissions are examined. Initially, neural networks (ANN) are employed to generate efficiency indicators (EI), such as average vehicle kilometers traveled (AVKM) and stock vehicles (SV), from socio-economic indicators (SEI). Subsequently, the energy demand of the Moroccan transport sector from 1990 to 2017 is modeled, and this model is validated using real data. Subsequently, energy demand from 2017 to 2050 is predicted, analyzing six scenarios: the first scenario serves as the reference scenario with-out EVs. Following this, four scenarios entail the integration of EVs with increasing penetration rates (10%, 30%, 80%, and 100% of new cars being EVs each year). Finally, scenario six corresponds to the conversion of the entire car fleet to EVs.