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Résumé : Educational Data Mining applies data-driven techniques to analyze learning patterns, optimize teaching strategies, and improve student outcomes in STEM education. This study investigates the role of educational data mining and adaptive learning technologies in enhancing instructional methodologies, predicting student disengagement, and personalizing learning experiences. Using a mixed-methods approach, data were collected from 1,483 students and 95 teachers through random and purposive sampling. The findings indicate adaptive learning technologies significantly improve student performance by tailoring instruction to individual needs. Real-time educational data analysis enables early identification of disengagement, facilitating timely interventions. Additionally, insights into student interaction patterns inform the development of evidence-based teaching strategies that foster critical thinking and problem-solving skills. The study highlights the transformative role of educational data mining in creating immersive learning environments that enhance conceptual understanding and practical application, reducing achievement gaps among diverse student populations. This research innovatively integrates adaptive learning technologies with real-time educational analytics to provide scalable, data-driven solutions for educators. The findings emphasize the potential of educational data mining-driven instructional strategies to enhance engagement, academic success, and equitable learning opportunities in STEM education.