The growing complexity of academic and career decision-making requires intelligent systems that can deliver personalized recommendations while ensuring strict compliance with institutional policies and incorporating evolving contextual factors. This paper introduces NeSy-Guidance, a neuro-symbolic recommendation approach that combines symbolic rule reasoning with graph-based neural inference over a dynamic and time-aware academic Knowledge Graph (KG). The model encodes students, programs, and contextual entities including regions and emerging trends while integrating regulatory constraints derived from Moroccan admission policies. It applies a two-stage reasoning pipeline: a symbolic layer enforcing hard eligibility rules and soft preference-based adjustments mined automatically from the knowledge graph, and a Graph Convolutional Network (GCN) layer trained with a weighted loss to address class imbalance and capture latent student–program compatibility. A weighted score fusion mechanism integrates both inference outputs, achieving a balance between interpretability, adaptability, and predictive performance. Evaluated on a real-world dataset of 800 students and 325 academic programs, NeSy-Guidance outperforms three state-of-the-art baselines in both accuracy and policy compliance. It achieves 83.8% accuracy, 74.5% precision@5, 75.4% F1-score, and ensures 100% compliance with institutional eligibility rules. Furthermore, a qualitative survey confirmed positive student satisfaction regarding the clarity and relevance of recommendations. These results demonstrate the effectiveness of hybrid reasoning and validate NeSy-Guidance as a reliable, explainable, and regulation-aware academic guidance system capable of adapting to regional disparities, emerging academic trends, and evolving student preferences.