This paper describes the design and implementation of a neural network-based Maximum Power Point Tracking (MPPT) system for a photovoltaic (PV) energy system. The proposed system utilizes the 1Soltech 1STH-215-P PV module, a buck DC/DC converter to regulate power flow, and a battery for energy storage to enhance system reliability and efficiency. The MPPT controller is developed using a neural network trained with historical solar irradiance and temperature data for Sarajevo, the capital city of Bosnia and Herzegovina. The data is sourced from NASA’s POWER database for the year 2024. The dataset provides hourly values, enabling precise modeling of the PV module’s performance under real-world conditions. The neural network MPPT block is designed to predict the optimal duty cycle of the DC/DC converter, ensuring maximum energy collection across different environmental conditions. The proposed system’s performance is validated through simulations that incorporate realistic PV module characteristics, including temperature coefficients and non-linear I-V characteristics curves. Comparative analysis with conventional MPPT methods, such as Perturb and Observe (P&O), demonstrates the superiority of the proposed neural network approach in terms of tracking accuracy, response time, and energy conversion efficiency. This work highlights the potential of machine learning techniques to enhance PV system performance and provides a robust framework for integrating neural network-based controllers with advanced data-driven energy management systems.