This paper presents NERDS-NITS’s sub mission to the WMT 2026 Low-Resource Arabic–Asian Language Translation Shared Task, which covers the following language pairs: Arabic↔English, Arabic↔Hindi, Arabic↔Bengali, Arabic↔Urdu, and Arabic↔Indonesian. For the three Indic language pairs, we propose a pivot-based framework that combines a fine-tuned Arabic↔English NLLB-200 model with pretrained IndicTrans2 checkpoints, motivated by the scarcity of direct Arabic–Indic parallel data. For Arabic↔Indonesian, where Indic Trans2 offers no coverage, we instead fine-tune NLLB-200-1.3B directly using QLoRA under memory-constrained conditions. Our Arabic↔English backbone, built on fine-tuned NLLB-200-600M model, forms the pivot leg for all Indic-language systems. Across all evaluated pairs, fine-tuning yields consistent improvements over zero-shot baselines, and the proposed pivot architecture further outperforms direct multilingual fine-tuning for Arabic–Indic pairs, with gains of up to 6.68 BLEU and 0.02 COMET