In order to prepare for or respond to a humanitarian disaster, crisis, and emergency, public officials and media organizations often must disseminate large amounts of technical information in a short amount of time. As the result, misinformation can circulate within or outside the affected community, and such misinformation can be particularly deadly during disaster scenarios. Therefore, it becomes a challenge for public safety agencies and organizations to reduce or eliminate the spread of misinformation on social media. The modern era of Artificial Intelligence (AI) based fact-checking models relies on machine learning (ML) models to detect misinformation using sophisticated algorithms. However, most of these ML approaches are limited by the data used to train them and they are over-dependent on being accessed via smartphone app interface which may be insufficient in low income countries, where more than eighty percent of the population do not have smartphones. Hence in this paper, we propose an integrated fact-checking system that relies on a large network of independent and crowdsourced volunteer “checkers” who collect, verify and upload any fake messages into an app, which also has the functionalities to offer anyone the ability to verify any message they have received. The app can receive messages for verification in form of short message system (SMS), app, and chatbot. In order to address the challenge of high illiteracy level in low-income countries, this platform is also able to support basic featurephones via an SMS-based interface where “verifiers” can send any suspicious message to a dedicated phone number as SMS and receive instant call alert which confirms the veracity or otherwise of the message. The platform relies on the shared intelligence of the “crowd” to train the machine learning models for a higher degree of accuracy, while also offering an instant automated call service, which is available as pre-recorded messages in twenty local languages.