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Recommender systems have gained significant attention as powerful tools for supporting decision-making processes in various domains. However, the understanding of their impact and application in the field of academic choices in higher education remains limited. This systematic review aims to provide a comprehensive summary of the current knowledge regarding recommender systems utilized in the context of academic choices and advising in higher education. The study is based on the systematic analysis of a set of primary studies (N = 56 out of 1578, published between 2011 and 2023) included according to defined criteria. The articles were categorized based on specific criteria, and their findings were analyzed and synthesized. Results show that the hybrid strategy has been the most effective method for producing recommendations. Evaluation measures such as offline experiments and case-study validation were prominently observed in the empirical studies, providing insights into the effectiveness of recommender systems. The findings reveal that the design of recommender systems in higher education is context-specific, with researchers considering various parameters to tailor recommendations to individual needs. However, most of the selected articles relied on lab-based studies rather than real-world applications, indicating a need for further research in practical settings. This systematic review also identifies future research directions, including the incorporation of deep learning technologies and the analysis of personality traits. By providing a comprehensive overview of the current state of recommender systems for academic choices in higher education, this review offers valuable insights for researchers and practitioners, guiding the development of more effective and personalized recommendation systems to unlock the full potential of individuals in their academic journey.