Access to accurate legal information is essential in today’s digital era, yet understanding and retrieving relevant legal content remains a challenging task for many users. Legal documents such as acts, sections, and case laws are often written in complex language, making them difficult for non-professionals to interpret. Traditional legal research methods rely heavily on manual searching through books, legal databases, or keyword-based search engines, which can be time-consuming and may produce irrelevant or incomplete results. To address these challenges, an AI-Based Legal Assistant system — LexAI — is developed using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) techniques. The system is implemented as a web-based application that allows users to enter legal queries in natural language and receive accurate, context-aware responses. It utilizes semantic analysis to understand user intent and retrieves relevant legal documents from a structured knowledge base using vector similarity search. The retrieved information is verified before generating responses, thereby reducing misinformation and improving reliability. By automating legal research and simplifying complex legal terminology, the proposed system enhances accessibility, reduces dependency on manual research, and provides an efficient and intelligent solution for legal information assistance.