<p>Multilingual Neural Machine Translation (MNMT) plays a vital role in extending language technologies to underrepresented and linguistically diverse regions. However, existing MNMT systems remain largely opaque, particularly when applied to structurally diverse and low-resource languages such as those of Northeast India. In this study, we propose an explainable MNMT framework tailored for bidirectional translation between English and six low-resource Indic languages—Assamese, Bodo, Khasi, Manipuri, Mizo, and Nepali—spanning Indo-Aryan, Tibeto-Burman, and Austroasiatic families. Our architecture employs a Transformer backbone augmented with language-conditioned adapter modules and sparsely activated Mixture-of-Experts (MoE) layers, enabling parameter sharing across languages while preserving family-specific specialization. The model was trained on curated parallel corpora with back-translation augmentation, achieving strong BLEU scores of 29.1 for Assamese and 26.1 for Nepali. To improve transparency, we integrated complementary post-hoc interpretability techniques—attention visualization, SHAP, and LIME—providing token-level and layer-wise explanations of translation decisions. Both quantitative and qualitative analyses show that these interpretability tools effectively diagnose translation challenges, reveal systematic biases, and elucidate failure modes, particularly for data-scarce languages such as Khasi and Mizo. Our results demonstrate that combining adapters, MoE-based specialization, and explainability can advance both the performance and trustworthiness of MNMT for low-resource Indic languages.</p>