This paper examines six categories of artificial intelligence techniques: deep learning techniques, reinforcement learning approaches, supervised learning models, unsupervised learning models, and deep learning–reinforcement learning combinations. It is simpler to examine the knowledge and research that has been used to AI in disaster management with these categories. Natural disasters can cause considerable destruction and financial losses, and in recent decades, actual losses and destruction have increased. Disaster managers must take proactive actions to protect their communities by creating effective management plans. AI technology is being used more and more to process data connected to disasters and enhance disaster management strategies. An overview of current AI applications in the four phases of disaster management—mitigation, readiness, response, and recovery—is given in this study. It provides real-world examples and a variety of AI-powered decision assistance tools to help with crisis management at each step. Notably, AI applications primarily focus on improving disaster response capabilities. The work also tackles significant obstacles in disaster response, promoting further progress in AI approaches to address these issues in future research.