This meta-analysis examines the impact of artificial intelligence (AI) technologies on personalized learning outcomes across educational contexts. Through systematic review of empirical studies published between 2015 and 2024, we synthesized findings from 42 studies meeting inclusion criteria to quantify the effectiveness of AI-driven personalization approaches. Results indicate a moderate positive effect (g = 0.61) of AI-based interventions on learning outcomes compared to traditional instruction. Particularly significant improvements were observed in STEM disciplines and among students with diverse learning needs. Feature selection techniques were applied to reduce dimensionality in the omics datasets associated with learning analytics, revealing key influencing factors. While promising, implementation challenges include technological infrastructure requirements, teacher training needs, and ethical considerations around data privacy. This research addresses significant gaps in understanding how specific AI mechanisms contribute to learning outcomes and identifies methodological limitations in existing literature. The findings provide a comprehensive framework for educational institutions and policymakers to make evidence-based decisions regarding AI integration in personalized learning environments.