In this paper, we present ANVITA machine translation system submitted to WMT 2026 shared task on Low-Resource Indic Language Translation, where the team participated for six translation directions: {Khasi, Nagamese, and Tagin} ↔ English. Two core ideas shaped the design of ANVITA system. Firstly, transfer learning by symmetric fine-tuning of T5-base model and enhanced cross-lingual transfer from related languages (Assamese to Nagamese) by mitigating script mismatch through transliteration. Secondly, to alleviate data scarcity, primary submissions are trained on datasets augmented with related language corpora and paraphrased pairs (English sentences), utilizing only the organizers provided datasets. For the contrastive submissions, training corpora are further expanded by compiling synthetic parallel data and related language data followed by corpora distillation with suitable selection strategies. ANVITA submissions are evaluated on the official test sets using multiple metrics which include BLEU, METEOR, TER, CHRF++, BERT score, and COMET. In the primary submissions, ANVITA achieved first rank for English→Tagin translation. For the contrastive submissions, ANVITA secured first rank for English → Khasi and Khasi → English with BLEU scores of 32.89 and 26.48 respectively. Furthermore, the contrastive submissions for Nagamese ↔ English and Tagin → English ranked second, underscoring efficacy of the data augmentation strategies applied.