The research aims to optimize and enhance the efficiency of the MPPT process using deep learning approaches. It focuses on the implementation of the MPPT system based on the Artificial Neural Network (ANN) of photovoltaic integrated into electrical vehicles. The ANN algorithms keep track of the accurate maximum point under varying environmental conditions. The adoption of alternative energy sources in electrical vehicles with advanced technology gives optimal performance and in addition reduces greenhouse emissions. Electrical Vehicles consume energy in Watthours/miles or Watthours/km. Power consumption of Electrical Vehicles depends on the vehicle’s size, weight, driving conditions, efficiency and speed. The deep learning controller utilizes inputs such as voltage, current or additional inputs temperature and solar irradiance to predict the optimal power output. Electric bicycles, electric scooters, electric ride-on toys, electrical skateboards and electrical wheelchairs consume below or up to 200 watts. The study highlights the adaptability of the MPPT system based on deep learning in electrical vehicles enhances the maximum energy utilization and system performance. The paper presents the deep learning training process, architecture and simulation results of the MPPT process of 200 watts using 2 inputs, 4 hidden layers, and 1 output.