The rapid advancement of digital technology has accelerated the transformation of educational systems through the integration of Artificial Intelligence (AI) and data analytics. In the context of twenty-first-century education, adaptive learning technologies are increasingly utilized to support personalized, learner-centered, and data-driven learning environments. This study aims to analyze the transformation of deep learning education through the integration of adaptive AI and data analytics, focusing on its opportunities, challenges, and implications for educational practice. The study employed a Narrative Literature Review (NLR) method by examining relevant literature from scientific journals, books, conference proceedings, policy documents, and institutional reports. Data were collected from reputable databases and analyzed using thematic analysis to identify major patterns and emerging themes. The findings indicate that adaptive AI enhances personalized learning by tailoring instructional content, feedback, and learning pathways to individual learner needs. Meanwhile, data analytics strengthens educational decision-making through evidence-based insights into learner performance and instructional effectiveness. However, challenges related to technological infrastructure, educator competencies, data privacy, algorithmic bias, and digital inequality remain significant barriers. Overall, the integration of adaptive AI and data analytics has substantial potential to create intelligent, effective, and sustainable educational ecosystems.