Medical negligence litigation in India poses a significant challenge to the justice system, often involving complex intersections of legal statutes and clinical evidence. These cases are frequently delayed due to manual document review, limited domain expertise among legal professionals, and lack of decision-support tools. This paper presents the design, development, and preliminary validation of a Judicial Decision Support System (JDSS) tailored to the Indian medico-legal context. The system employs state-of-the-art Natural Language Processing (NLP) techniques and supervised machine learning to automate triage and recommendation tasks. Specifically, it performs case summarization, IPC section prediction, and precedent retrieval using transformer-based models fine-tuned on Indian legal corpora. Architecture integrates domain-specific ontologies and is evaluated through expert workshops involving legal and medical professionals. Initial feedback indicates promising utility in accelerating case screening and improving interpretability of legal-medical data. This work contributes to the growing field of legal informatics and lays the groundwork for AI-assisted judicial tools in low-resource legal ecosystems.