Image captioning is a challenging task that involves generating descriptive textual content for visual inputs, and it becomes even more complex when extended to low-resource languages. Tamil, a Dravidian language spoken by millions, is underrepresented in current image captioning datasets and frameworks, which limits the development of AI systems tailored for this community. To address this gap, we propose the Culturally Adaptive Bilingual Image Captioning (CABIC) framework, which generates captions in English and Tamil with an emphasis on cultural relevance. The proposed framework integrates Inception V3 for visual feature extraction, XLM-R for multilingual textual embeddings, and an attention mechanism for effective multimodal fusion. Additionally, a novel cultural adaptation module refines Tamil captions to incorporate idiomatic expressions and contextual nuances. Evaluations on the TamilCOCO dataset demonstrate that CABIC outperforms state-of-the-art models, achieving a BLEU-4 score of 35.6, METEOR of 29.4, CIDEr of 120.1, SPICE of 20.3, and a Cultural Relevance Score (CRS) of 85.6. Qualitative analysis highlights CABIC’s ability to generate culturally enriched captions while identifying areas for improvement. This research bridges the gap in Tamil image captioning, offering a robust framework for bilingual and culturally aware AI systems and paving the way for future advancements in low-resource language technologies.