This chapter highlights the key challenges of our ongoing project in developing an information technology solution for emergency response and management in smart cities. We aim to develop a cloud-based big data framework that will enable us to utilize heterogeneous data sources and sophisticated machine learning techniques to gather, process, and integrate information intelligently to support emergency response to any disaster or crisis rapidly. After identifying the right data sources, we turn our attentions into investigating suitable techniques that can be utilized in disaster-event detection as well as extraction and representation of useful features related to the disaster. We also outline our approach in analysis and integration of disaster-related knowledge with the help of a disaster ontology. Our ultimate goal is to display and disseminate actionable information to the decision-makers in the format most appropriate for carrying out emergency response and coordination efficiently. We developed a dashboard-like interface to facilitate such goal. For any disaster or emergency, the heterogeneous nature (texts, image, audio, and videos) and sheer volume of data instantly available on the social media platforms necessitate fast and automated processing (including integration and fusion of information originating from disparate sources). This chapter highlights our ongoing research in addressing such challenges in an automated fashion using state-of-the-art artificial intelligence and machine learning techniques suitable for processing multimodal social-media data. Our research contributions will eventually facilitate building a comprehensive disaster management framework and system that may streamline emergency response operations in the smart cities.