This paper describe DELAB-IIITM’s submission system for the WMT25 machine translation shared task. We participated in two sub-task of the Indic Translation Task, en↔as and en↔mn i.e. Assamese (Indo Aryan language) and Manipuri (Tibeto Burman language) with a total of six translation directions, including mn→en, mn←en, en→as, en←as, mn→as, mn←as. Our fine tuning process aims to leverages the pretrained multilingual NLLB-200 model, a machine translation model developed by Meta AI as part of the No Language Left Behind (NLLB) project, through two main development, Synthetic parallel corpus creation and Strategic Fine-tuning. The Fine-tuning process involves strict data cleaning protocols, Adafactor optimizer with low learning rate(2e-5), 2 training epochs, train-test data splits to prevent overfitting, and Seq2SeqTrainer framework. The official test data was used to generate the target language with our fine-tuned model. Experimental results show that our method improves the BLEU scores for translation of these two language pairs. These findings confirm that back-translation remains challenging, largely due to morphological complexity and limited data availability.