This paper presents a systematic literature review of using Machine Learning (ML) techniques in higher education career recommendation. Despite the growing interest in leveraging Artificial Intelligence (AI) for personalized academic guidance, no previous reviews have synthesized the diverse methodologies in this field. Following the Kitchenham methodology, we analyzed 38 studies selected from an initial pool of 1,296 articles, retrieved using a custom-built web scraper leveraging the CrossRef API. Data were extracted based on ML techniques, data types, and validation metrics. Our findings reveal that Random Forest, Support Vector Machines (SVM), and Neural Networks are the most frequently employed models to improve the accuracy and personalization of career recommendations in higher education. These systems typically use academic performance, personal interests, and demographic data as the primary data types. The review also highlights key validation metrics like precision, recall, and F1-score, which reflect the effectiveness of these models. However, limitations were identified, such as the lack of access to open datasets and the scarcity of studies with longitudinal data that evaluate the long-term impact of recommendations. Additionally, ethical considerations, particularly regarding fairness, transparency, and data privacy, were highlighted as critical challenges. This systematic literature review provides a solid foundation for improving career recommendation systems using advanced ML techniques. By integrating ML with traditional counseling approaches, this research underscores the potential to revolutionize academic guidance and better align students with their career goals.