AI-driven personalization systems rely on data processing, natural language understanding, and adaptive pattern recognition to tailor recommendations to individual users. However, personalization across multilingual contexts requires not only translation but cultural, linguistic, and semantic alignment. This research explores how AI systems interpret language-embedded cultural cues, emotion markers, dialect variations, and contextual meaning to deliver personalized content across diverse linguistic ecosystems. The paper introduces the Multilingual Personalization Architecture (MPA), integrating machine translation, language models, cultural context encoding, and user profiling. Results highlight challenges including semantic drift, cultural bias, polysemy, emotion misclassification, and regional identity cues embedded in language.