<p>This survey presents a comprehensive overview of Legal Natural Language Processing (NLP) in the Indian context, with a focus on linguistic diversity across Indian languages and challenges related to equitable access to legal resources. Based on a decade-long analysis of law-centric literature, we trace the evolution of legal NLP research in India, highlighting the adoption of deep learning architectures, pre-trained language models, and domain-specific embeddings. We identify key application areas, such as legal named entity recognition, judgment prediction, legal question answering, case summarization and other key legal NLP tasks. The survey also emphasizes the need for open data sets and reproducible code to foster transparency and scalability in legal NLP research. This work aims to inform researchers, practitioners, and policymakers about current developments, challenges, and future directions in this rapidly evolving field.</p>