This paper describes the submission of team JH_NLP_APAShreya2 to the English–Manipuri (Bengali-script) track of the WMT 2026 Low-Resource Indic Machine translation shared task. Our system adapts the multilingual NLLB-200-distilled-600M model using Low-Rank Adaptation (LoRA) on the official English-Manipuri parallel corpus. The corpus contains 23,687 sentence pairs. We used no external parallel or monolingual data, synthetic data, or back-translation. We conducted a progressive sequence of experiments with different data-pool sizes and training durations. The final model was trained for seven epochs and used to translate all 1,000 English source sentences in the official blind test set. The primary submission obtained 8.03 BLEU, 20.15 METEOR, 82.56 TER, 40.93 chrF++, 84.86 BERTScore, and 68.18 COMET. The system ranked within the top three primary systems according to five of the six reported evaluation metrics. These results indicate that parameter-efficient adaptation of multilingual models is a practical approach to low-resource English-Manipuri machine translation.