In a massively multilingual country like India, providing legal judgments in understandable native languages is essential for equitable justice to all. The Legal Machine Translation (L-MT) shared task focuses on translating legal content from English to Hindi which is the most spoken language in India. We present a comprehensive evaluation of neural machine translation models for English-Hindi legal document 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 statutory structure, legal citations, and jurisdictional terminology. We fine-tune two legal focused translation models, InLegalTrans and IndicTrans2 on the English-Hindi legal parallel corpus provided by the organizers where the use of any external data is constrained. The fine-tuned InLegalTrans model achieves the highest BLEU score of 0.48. Comparative analysis reveals that domain adaptation through fine-tuning on legal corpora significantly enhances translation quality for specialized legal texts. Human evaluation confirms superior coherence and judicial tone preservation in InLegalTrans outputs. Our best performing model is ranked 3rd on the test data.