Figurative language understanding remains a significant challenge for Large Language Models (LLMs), especially for low-resource languages. To address this, we introduce the Bangla Bagdhara dataset, a large-scale, culturally grounded corpus of 10,361 Bengali idioms. Each idiom is annotated under a comprehensive 19-field schema, established and refined through a deliberative expert consensus process that captures its semantic, syntactic, cultural, and religious dimensions, providing a rich and structured resource for computational linguistics. To establish a robust benchmark for Bangla figurative language understanding, we evaluate 30 state-of-the-art multilingual and instruction-tuned LLMs on the task of inferring figurative meaning. Our results reveal a critical performance gap, with no model surpassing 50% accuracy, in stark contrast to significantly higher human performance (83.4%). This finding underscores the limitations of existing models in cross-linguistic and cultural reasoning. By releasing the Bangla Bagdhara dataset and benchmark, we provide foundational infrastructure for advancing figurative language understanding and cultural grounding in LLMs for Bengali and other low-resource languages.