An Intelligent Lakehouse-Based Data Analytics and Query System is an intelligent data analytics platform designed to simplify the process of extracting meaningful insights from large and complex datasets by integrating Artificial Intelligence with modern data architecture principles. Traditional data analysis systems require users to possess strong technical knowledge and write complex SQL queries, which creates a significant barrier for non-technical users and slows down decision-making. This project addresses these challenges by enabling users to input queries in natural language, which are automatically converted into optimized SQL queries using an AI-based query engine. The system is built on a Lakehouse architecture, which combines the scalability and flexibility of data lakes with the performance and structure of data warehouses, allowing efficient storage and processing of structured, semi-structured, and unstructured data. It incorporates multiple processing engines such as DuckDB for fast SQL-based analytics, Polars for high-performance data processing, and PySpark for handling large-scale distributed datasets, ensuring optimal performance based on the size and complexity of the data. Additionally, the platform includes a smart query validation mechanism to correct errors, a suggestion system to guide users in query formulation, and an intelligent query planner that selects the most suitable execution engine. The results are presented through interactive dashboards featuring KPI visualizations, charts, and dynamic filters, making data interpretation intuitive and accessible. By combining AI-driven query processing, multi-engine execution, and user-friendly visualization, Insight Lake AI reduces the complexity of data analytics, improves efficiency, and enhances decision-making capabilities. This project demonstrates how intelligent systems can effectively bridge the gap between raw data and actionable insights, making advanced analytics accessible, faster, and more efficient for a wide range of users.