In a massively multilingual country like India,providing legal judgments in understandablenative languages is essential for equitable jus-tice to all. The Legal Machine Translation(L-MT) shared task focuses on translating le-gal content from English to Hindi which is themost spoken language in India. We present acomprehensive evaluation of neural machinetranslation models for English-Hindi legal doc-ument translation, developed as part of the L-MT shared task. We investigate four multi-lingual and Indic focused translation systems.Our approach emphasizes domain specific fine-tuning on legal corpus while preserving statu-tory structure, legal citations, and jurisdic-tional terminology. We fine-tune two legalfocused translation models, InLegalTrans andIndicTrans2 on the English-Hindi legal paral-lel corpus provided by the organizers wherethe use of any external data is constrained.The fine-tuned InLegalTrans model achievesthe highest BLEU score of 0.48. Compara-tive analysis reveals that domain adaptationthrough fine-tuning on legal corpora signifi-cantly enhances translation quality for special-ized legal texts. Human evaluation confirmssuperior coherence and judicial tone preserva-tion in InLegalTrans outputs. Our best per-forming model is ranked 3rd on the test data.