The rapid growth of electric vehicles (EVs) has created a strong demand for efficient and fast charging solutions. However, conventional charging methods are time-consuming and place significant stress on the power grid when deployed on large scale. To address these challenges, this study proposes a standalone solar photovoltaic (PV)-based DC microgrid for fast EV charging. The system is designed to regulate charging using a DC-DC boost converter controlled by two strategies: a conventional Proportional-Integral (PI) controller and an Artificial Neural Network (ANN)-based controller. A detailed simulation model is developed in MATLAB/Simulink, including PV system parameters, converter specifications, and a lithium-ion battery modeled using a Thevenin equivalent circuit. The ANN controller is trained using real-time operating conditions such as irradiance, temperature, and state of charge (SoC). Performance is evaluated based on transient response, overshoot, settling time, steady-state error, and total harmonic distortion (THD). Results show that the ANN controller significantly improves system performance. Voltage overshoot is reduced from 10% to 2%, current overshoot from 20% to 4%, and THD from 6.8% to 2.1%. Additionally, the settling time is improved by approximately 57% compared to the PI controller. These findings demonstrate that AI-based control strategies provide a more efficient, stable, and reliable solution for renewable energy-based EV charging systems. The ANN controller reduced voltage overshoot from 10% to 2%, current overshoot from 20% to 4%, and THD from 6.8% to 2.1%, while improving settling time by up to 57%.