This paper investigates religious bias in multilingual large language models, particularly focusing on South Asian languages like Bengali. It introduces BRAND, a bilingual dataset of over 2,400 religious norms, revealing that models exhibit significant biases, perform better in English, and consistently show favoritism towards Islam in Bengali, highlighting critical challenges for fair AI in diverse linguistic and religious contexts.