In MATLAB, designing a Hybrid Neural Network Incremental Conductance Maximum Power Point Tracking (MPPT) system involves integrating neural network techniques with the Incremental Conductance method to enhance MPPT performance in photovoltaic (PV) systems. This hybrid approach combines the adaptive learning capabilities of neural networks with the precise tracking abilities of Incremental Conductance, resulting in improved efficiency and accuracy in identifying and maintaining the maximum power point (MPP) of the PV array under varying environmental conditions. The neural network component learns and adapts to the nonlinear characteristics of the PV system, while the Incremental Conductance algorithm provides real-time adjustments to track changes in the MPP. By implementing the Hybrid Neural Network Incremental Conductance MPPT algorithm in MATLAB, engineers can simulate and optimize its performance, fine-tuning neural network parameters and Incremental Conductance control logic to achieve faster and more accurate MPP tracking. MATLAB's extensive computational tools and simulation capabilities support the development of advanced MPPT strategies, contributing to the efficient utilization of solar energy in PV systems.