This paper investigates the development of predictive models to identify students at academic risk in higher education, even in scenarios with incomplete historical data. Using real data provided by Monterrey Institute of Technology and Higher Education, we simulated four distinct scenarios with varying levels of information availability, reflecting real-world situations in the educational context. The methodology adopted included exploratory analysis for feature selection and engineering, sampling techniques for class balancing, and the application of several machine learning classifiers with default settings. The database used contains academic and sociodemographic information of 1.796 unique students. Eighty model combinations were evaluated, using the AUC-ROC metric as the main performance indicator. The four scenarios were designed to progressively incorporate different categories of student information, ranging from demographics and admissions data to engagement and academic performance metrics, and allow us to analyze the impact of information completeness on model performance. The results indicate that even with limited data, it is possible to achieve competitive predictive performance (AUC ≈ 0.87), whereas scenarios with complete academic records achieve AUCs of up to 0.96. Variables such as current academic performance, admissions test scores, study load, and demographic attributes were consistently influential. These findings reinforce the possibility of early prediction of academic risk and provide a flexible framework for adapting predictive strategies based on available data.