In today’s digital world, customer support is evolving rapidly, with businesses turning to intelligent chatbots to enhance user experiences. This project focuses on creating a smart Customer Support Chatbot that understands user queries, provides accurate responses, and improves customer satisfaction all while keeping operational costs low. By using advanced Natural Language Processing (NLP) techniques and sending the patterns to proposed Long-Short Term Model (LSTM) networks the chatbot can recognize user intent, maintain context awareness, and even analyze sentiment, making interactions more natural and engaging. The system is built on a microservices architecture, ensuring flexibility and scalability, while secure APIs safeguard data and enable real-time query handling. To ensure top performance, the chatbot has undergone extensive testing, measuring response accuracy and processing speed. The results show significant improvements in traditional customer support systems, making it a reliable and efficient solution for modern businesses.