This study presents a neural network-based model specifically designed to optimize the sizing of photovoltaic (PV) systems for industrial applications, focusing on maximizing self-consumption and enhancing return on investment. Given the growing role of solar PV in the energy transition, effective sizing of installations is essential to balance energy generation, economic return, and environmental sustainability. By leveraging local solar irradiation data, monthly demand profiles, and specific PV configurations, the model predicts the optimal PV capacity across diverse commercial and industrial scenarios in various regions of Spain. Scenario-based testing demonstrates the model’s accuracy in aligning PV output with site-specific demand, contributing to reduced grid dependency and operational costs. This adaptable approach offers a practical solution for businesses aiming for energy autonomy and eco nomic efficiency through tailored PV system designs, underscoring the role of neural networks in advancing energy management strategies.