In this diverse world, language barriers pose significant challenges in sectors such as tourism and medicine, affecting the efficiency in communication and information availability. In context of tourism, diverse linguistic norms often affect tourist experience and satisfaction. This paper proposes a novel solution to address information scarcity and asymmetry - a Cross-Lingual Open retrieval Question Answering (CLOR-QA) system. This model, trained on English, Chinese, Vietnamese, Arabic, and German, facilitates cross-lingual information retrieval, enabling users to access data in languages different from the query language by integrating a cross-lingual approach into the question-answering system. The user can access the multilingual data entered in real time to get prompt responses through the proposed CLOR-QA model, this system utilizes Helsinki-NLP model to bridge gaps between different languages. Moreover, it integrates BERT-based QA models customized for each language, enhancing precision in extracting answers by effectively capturing language-specific intricacies. The CLOR-QA system offers a promising solution to overcome language barriers by improving the accessibility of cross-lingual information.