<p>Virtual Reality (VR) has emerged as a transformative technology in educational management, offering immersive and interactive environments for training, decision-making, and organizational preparedness. However, successful adoption of VR in education remains challenged by high implementation costs, limited readiness analysis, and the absence of robust predictive models. This research proposes an Improved Glowworm Optimization–based Decision Tree (IGWO-DT) model to evaluate and predict VR adoption success in educational management training. The model employs IGWO for optimal feature selection capturing key institutional indicators such as infrastructure readiness, faculty training, funding, and student engagement and integrates these with Decision Tree classification to ensure interpretability and reliable predictions. Experimental validation using a Kaggle dataset, processed with Z-score normalization and evaluated, demonstrates that the IGWO-DT model achieves superior outcomes compared to baseline methods, with 97% accuracy. These results confirm that the proposed approach enhances predictive reliability, improves classification robustness, and supports strategic planning for VR adoption in education. This research contributes a data-driven, interpretable framework to guide institutions in effective technology integration and management reorganization.</p>