Analysis of 18 studies reveals AI-guided semaglutide therapy delivers superior glycated hemoglobin reduction, weight loss, and cardiovascular benefits compared to standard fixed-dose protocols. The research identifies four key AI applications: patient risk stratification, body composition imaging analysis, cardiovascular assessment, and personalized dosage optimization. This represents a meaningful step toward precision medicine in diabetes care, where treatment intensity can be tailored to individual patient profiles rather than following one-size-fits-all protocols. The potential is substantial given semaglutide's proven track record and the growing sophistication of wearable health monitoring. However, this systematic review highlights critical implementation barriers that temper immediate clinical optimism. The evidence base remains limited to small-scale studies without the large randomized controlled trials needed for regulatory approval. Current AI models lack the explainability required for physician confidence and regulatory validation. Most critically, no clear pathway exists for integrating these systems into routine clinical practice. While AI-driven personalization represents the future of diabetes management, the field requires substantial additional research and infrastructure development before these promising laboratory findings translate into widespread patient benefit.