The paper proposes a novel approach that integrates machine learning and NLP in order to identify phishing sites and create multilingual interaction by providing better user engagement through the chatbot. It will utilize the XG-Boost algorithm in order to show the phishing detection system with more than 90% accuracy rate in the identification and classification of websites as legitimate or phishing for a set of 10,000 websites. A major contribution of this work is the embedding of a multilingual chatbot, developed on Dialog- flow, with support for English, Hindi, and Marathi, thereby broadening the possible community of users for the system. This paper describes the architecture of the system at its different layers- feature extraction, model training, and its integration with the chatbot. The proposed work fills the gaps in earlier literature as it provides a user interface accompanied by robust detection. This system will also be extended by the provision of language support by adding more languages in the near future and also by increasing the detection accuracy using deep learning models. The results demonstrate that combining machine learning with user-centric design may improve the detection of phishing sites considerably and enhance user engagement.