Natural Language Processing (NLP) is transforming legal firms by enhancing legal text analysis, legal document management, and judicial decision prediction. Conventional rule-based and statistical methods lack the contextual understanding, and scalability required for processing complex legal texts, while deep learning and transformer-based models have revolutionized advanced Legal Artificial Intelligence (LegalAI) technologies. Large Language Models (LLMs), including BERT, GPT, LLaMA, and domain-specific transformers like Legal-BERT and CaseLaw-BERT, have refined the state-of-art models in legal NLP tasks like legal text classification, legal text summarization, and judgment prediction. This study analyzes 40 selected journals and conference papers from 2017 to 2024, emphasizing the developing research interest in LLM-based legal applications. Major developments consist of hierarchical transformers, rhetorical role classification, and legal knowledge graphs that facilitate legal text parsing and logical inference. This paper spans intellectual breakthroughs with real-world applications by reviewing LLMs and Knowledge Graphs (KG) for legal NLP, providing key findings for scholars and experts working on AI-driven legal systems.