Academic and career guidance is crucial for student success. This systematic review examines recent advancements in recommender systems (RS) designed to support students’ academic and career decision-making. Following PRISMA 2020 guidelines, we analyzed 21 peer-reviewed papers published between 2020 and 2025, focusing on methodologies, effectiveness, evaluation metrics, challenges, and emerging trends. Our findings reveal a dominance of knowledge-based and hybrid approaches, leveraging machine learning techniques. While these systems demonstrate high accuracy (average 86.4%) in predicting suitable career paths and educational programs, significant challenges remain. These include integrating real-time labor market data, enhancing personalization beyond academic performance, addressing data sparsity and the cold-start problem, ensuring transparency and interpretability of recommendations, and adapting to evolving student preferences and career landscapes. Emerging trends highlight the potential of deep learning, knowledge graphs, and social media data integration. Future research should focus on enhancing model interpretability, reducing biases, and developing adaptive frameworks that align with evolving student needs and labor market demands. Advancing these systems will strengthen their role in supporting students’ academic and career decision-making.