Cultural lacunae are lexical or conceptual gaps where a culture-bound term has no direct equivalent in another language. Traditional identification methods are subjective and non-scalable. This study proposes a corpus-driven pipeline for automatic detection of cultural lacunae using corpus tools and cross-lingual embeddings. Two comparable corpora (American English and Uzbek, 5 million words each) were constructed. The pipeline detected 147 candidate lacunae with strict precision of 72% and lenient precision of 88.5%. Food, social rituals, and legal-administrative domains showed the highest lacuna density. Building on Ataboev's (2019a, 2019b, 2020, 2024a, 2024b) corpus linguistics research, this study extends automatic detection to cultural gap identification.