We propose a region-aware hybrid retrieval framework for culturally grounded multilingual question answering. Our system combines BM25-based lexical matching with dense semantic similarity using sentence embeddings, integrating both signals into a unified ranking function. To further prioritize culturally relevant evidence, we introduce a regional weighting heuristic that boosts documents containing explicit region-specific references. The top-ranked evidence passages are incorporated into a structured prompt and processed by a 4-bit quantized Qwen3-14B model. Instead of generating free-form text, the model selects answers deterministically using a logit-based scoring mechanism over the four multiple-choice options. This design enables efficient inference while improving cross-lingual stability, particularly in culturally explicit contexts.