This research deals with the modeling of a Multi-Layers Feed Forward Artificial Neural Networks (MLFFNN), trained using Gradient Descent algorithm with Momentum factor & adaptive learning rate, to estimate the output of the neural network corresponding to the optimal Duty Cycle of DC-DC Boost Converter to track the Maximum Power Point of Photovoltaic Energy Systems. Thus, the DMPPT-ANN “Developed MPPT-ANN” controller proposed in this research, independent in his work on the use of electrical measurements output of PV system to determine the duty cycle, and without the need to use a Proportional-Integrative Controller to control the cycle of the work of the of DC-DC Boost Converter, and this improves the dynamic performance of the proposed controller to determine the optimal Duty Cycle accurately and quickly. In this context, this research discusses the optimal selection of the proposed MLFFNN structure in the research in terms of determining the optimum number of hidden layers and the optimal number of neurons in them, evaluating the values of the Mean square error and the resulting Correlation Coefficient after each training of the neural network. The final network model with the optimal structure is then adopted to form the DMPPT-ANN Controller to track the MPP point of the PV system. The simulation results performed in the Matlab / Simulink environment demonstrated the best performance of the proposed DMPPT-ANN controller based on the MLFFNN neural network model, by accurately estimating the Duty Cycle and improving the response speed of the PV system output to MPP access, , as well as finally eliminating the resulting oscillations in the steady state of the Power response curve of PV system compared with the use of a number of reference controls: an advanced tracking controller MPPT-ANN-PI based on ANN network to estimate MPP point voltage with conventional PI controller, a MPPT-FLC and a conventional MPPT-INC uses the Incremental Conductance technique INC