Integration of Generative AI in Mern Stack for Personalized E-Commerce Systems

Abstract- This research focuses on developing an AI-Powered smart e-commerce platform that provides a highly personalized, efficient, and user-centric online shopping experience. The proposed system leverages artificial intelligence and machine learning algorithms to understand customer behaviour, predict preferences, and intelligently recommend products. Built using the MERN stack (MongoDB, Express.js, React.js, and Node.js), the platform ensures scalability, high performance, and real-time responsiveness. The system integrates generative AI to improve product discovery through intelligent chat bot interactions, allowing users to search for products using natural language queries instead of traditional keyword searches. This conversational interface improves reach and engagement while reducing search complexity. A recommendation engine trained on customer interaction data provides personalized suggestions, increasing the likelihood of conversion and customer satisfaction. The backend uses AI powered data analysis to identify market trends, predict demand, and support data-driven business decisions. Additionally, the platform emphasizes security, scalability, and a modular architecture to accommodate future expansions, such as augmented reality (AR) product previews. The system's performance was evaluated based on response time, recommendation accuracy, and user satisfaction, demonstrating significant improvements compared to traditional e-commerce platforms. Overall, the proposed AI-powered e-commerce system demonstrates how the combination of generative AI, machine learning, and modern web technologies can revolutionize online retail. This not only enhances user engagement and personalization, but also provides businesses with valuable insights to optimize operations and improve profitability. Keyword- Artificial Intelligence (AI), Generative AI, Machine Learning (ML), MERN Stack, Web App