In this paper, an optimized maximum power point tracking (MPPT) control for a standalone photovoltaic (PV) system using a three-level boost (TLB) converter is introduced. The proposed MPPT method is based on an intelligent perturb and observe algorithm using the artificial neural network (ANN-P&O) to reduce the oscillations at the maximum power point (MPP). In advance, The ANN provides the values of the voltage and the current at the MPP for any solar irradiance and cell temperature. Based on the provided voltage and current, the P&O algorithm generates the optimal duty cycle of the TLB converter to perfectly track the MPP of the PV generator for different values of cell temperature and sunlight irradiance. Besides, a proportional-integral (PI) controller is added to ensure the TLB capacitor voltage balance. The established ANN-P&O approach is validated in Matlab/Simulink and compared to the conventional P&O algorithm under various scenarios: (i) irradiance variations, (ii) temperature variations, and (iii) load variations.