The education sector has witnessed significant advancement in adopting Artificial Intelligence techniques to improve learning outcomes. One of the most prominent applications is building predictive models based on neural networks to analyze students’ academic performance. These models aim to predict student achievement levels using historical data such as attendance, grades, class participation, and learning behavior. Artificial neural networks simulate the structure of the human brain through interconnected layers of digital neurons capable of detecting complex patterns within educational data. After training on historical datasets, the model can accurately predict the likelihood of academic success or failure. The importance of such models lies in enabling early intervention for at-risk students through personalized academic support programs. Additionally, they enhance strategic educational planning and data-driven decision-making. Despite these benefits, the development of such systems requires strict adherence to data privacy and ethical standards. Therefore, integrating neural networks into academic performance analysis represents a strategic move toward smarter and more proactive education systems.