Around the globe, numerous communities still suffer from burgeoning natural and human-made disasters, which gives rise to a realization that more effective disaster management is required. The research problem is as- sociated with the existing research gap in terms of the integration of artificial intelligence into disaster management. The purpose of the present study is to focus on the employment of AI in disaster management while concentrating on predictive abilities and resource optimization. A mixed-methods approach has been used, which integrates a systematic literature review and quantitative analysis of six case studies concerning the application of AI tools in disaster situations. The key findings that have been obtained give way to several critical outcomes. First and foremost, machine learning algorithms improve prediction with reference to natural disasters up to 25% in comparison with traditional models. As a result, the application of AI diminishes the time of the needed response significantly. Additionally, AI-driven systems such as drones and predictive analytics optimize the allocation of resources up to 30%. At the same time, the amounts of resources have not been increased; rather, they have been distributed more appropriately and timely. In terms of the social and ethical aspects of applying AI in crisis situations, such as concerns about privacy and proper division of resources within the population, it is stressed that the great reliance should be on AI not only to improve disaster management and general readiness but also to ensure that post-disaster recovery efforts become prompt. For this reason, research should be ongoing, and collaboration among practitioners, researchers, and policy makers is possible and needed.