This article examines the representation of cultural markers in contemporary machine translation platforms, focusing on the challenges associated with preserving their semantic, pragmatic, and linguocultural characteristics in cross-linguistic communication. The study explores the theoretical foundations of cultural markers, their classification within translation studies, and the problems of cultural equivalence arising in automated translation systems. Particular attention is paid to the linguistic analysis of culture-specific concepts, idiomatic expressions, proverbs, forms of address, and references to traditional ceremonies in Uzbek and English, illustrated through representative examples. The research proposes a methodological framework for evaluating the translation of culturally marked expressions in Google Translate, DeepL, and Microsoft Translator according to four criteria: semantic accuracy, cultural equivalence, pragmatic appropriateness, and linguistic naturalness. The qualitative analysis demonstrates that literal translation does not always ensure the adequate transmission of culturally embedded meanings and that descriptive translation, functional equivalence, and contextual explanation may be necessary to preserve the communicative significance of culturally specific expressions. The study emphasizes the importance of integrating linguistic and sociocultural dimensions into machine translation assessment. Its scientific contribution lies in developing a comprehensive analytical approach to evaluating cultural adequacy in automated translation and providing a theoretical foundation for improving the culturally sensitive application of artificial intelligence technologies in multilingual communication.

cultural markers, machine translation, linguoculturology, cultural equivalence, neural machine translation, artificial intelligence, semantic accuracy, pragmatic appropriateness, idiomatic expressions, translation strategies.

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