<![CDATA[This systematic review investigates the convergence of cloud computing, learning analytics, and artificial intelligence (AI) in enabling personalized learning within cloud-based environments. The study aims to map the intellectual structure, methodological trends, and core challenges within this interdisciplinary domain. Adhering to the PRISMA 2020 guidelines, we analyzed 40 seminal publications (2017-2025) sourced primarily from Scopus. Our analysis reveals a field dominated by a potent triad synergy, where scalable cloud infrastructure serves as the foundational enabler, learning analytics acts as the central nervous system for generating insights, and AI forms the intelligent core for driving pedagogical adaptations. However, the results identify significant imbalances: a predominant focus on technical feasibility and technology adoption models (e.g., TAM) overshadows deeper grounding in learning theories, while a pronounced geographical bias towards the US and China limits the generalizability of findings. The discussion underscores that the connection between data-driven insights and pedagogical execution often remains mechanistic, and ethical considerations, though emerging, require more robust, context-sensitive frameworks. We conclude that the field must evolve from technical demonstration towards pedagogically-grounded, ethically-aware, and empirically-validated research. Future directions should prioritize longitudinal studies, the integration of collaborative learning models, and investigations in underrepresented regions to ensure the development of equitable and effective AI-driven personalization for a global audience.]]>
