This article presents the development of an academic management model integrating artificial intelligence, aimed at the early prediction of school failure risk. The study employed scientifically validated psychometric instruments, from which multi-stage databases were constructed, incorporating variables related to reading comprehension, mental health, spelling, and mathematical performance. Regression and clustering algorithms were compared with traditional linear approaches, showing a substantial enhancement in predictive performance. The proposed model is designed to support institutional data-driven decision-making and to guide timely pedagogical interventions. The findings confirm the high potential of artificial intelligence as a tool to strengthen educational inclusion, optimize academic management processes, and improve school performance in public education contexts marked by socioeconomic vulnerability. These findings lay the groundwork for scalable, ethical AI systems supporting inclusive education.
