The integration of intent detection and generative AI (GenAI) significantly enhances customer support systems by enabling more accurate and efficient handling of customer inquiries. This paper explores how these technologies can transform customer support by understanding user intent and generating personalized responses. Intent detection, powered by natural language processing (NLP) techniques such as tokenization, part-of-speech tagging, and named entity recognition, allows systems to identify the underlying purpose of customer queries. Machine learning models, including Support Vector Machines (SVM), Decision Trees, Recurrent Neural Networks (RNN), and Transformers, are trained on labeled datasets to classify intents and provide accurate responses. Generative AI, using advanced language models like GPT-4, generates contextually relevant and personalized responses, with pre-trained models fine-tuned for domain-specific data to improve accuracy. These technologies offer real-time support, personalization, and scalability, allowing businesses to handle high volumes of inquiries efficiently. However, challenges include data quality, model maintenance, and ethical considerations such as data privacy and bias. Future advancements in NLP, multilingual support, and integration with other technologies like voice assistants and augmented reality promise further improvements in customer support. The paper concludes that the adoption of intent detection and GenAI can significantly enhance customer satisfaction and operational efficiency, despite ongoing challenges.The integration of intent detection and generative AI (GenAI) significantly enhances customer support systems by enabling more accurate and efficient handling of customer inquiries. This paper explores how these technologies can transform customer support by understanding user intent and generating personalized responses. Intent detection, powered by natural language processing (NLP) techniques such as tokenization, part-of-speech tagging, and named entity recognition, allows systems to identify the underlying purpose of customer queries. Machine learning models, including Support Vector Machines (SVM), Decision Trees, Recurrent Neural Networks (RNN), and Transformers, are trained on labeled datasets to classify intents and provide accurate responses. Generative AI, using advanced language models like GPT-4, generates contextually relevant and personalized responses, with pre-trained models fine-tuned for domain-specific data to improve accuracy. These technologies offer real-time support, personalization, and scalability, allowing businesses to handle high volumes of inquiries efficiently. However, challenges include data quality, model maintenance, and ethical considerations such as data privacy and bias. Future advancements in NLP, multilingual support, and integration with other technologies like voice assistants and augmented reality promise further improvements in customer support. The paper concludes that the adoption of intent detection and GenAI can significantly enhance customer satisfaction and operational efficiency, despite ongoing challenges.