As climate change intensifies, the frequency and severity of natural hazards continue to rise, resulting in severe socioeconomic impacts globally, including health impairments, economic losses, and disruption to livelihoods. In response to these challenges, innovative disaster management solutions are essential for reducing impacts and enhancing resilience. Recent advances in artificial intelligence (AI), particularly deep learning (DL) and also machine learning (ML), offer transformative potential in disaster management, involving pre-disaster preparedness, real-time response, and post-disaster recovery. This chapter provides a comprehensive examination of machine learning and deep learning’s role and future potential in minimizing disaster impacts and improving management efficiency. Starting with an overview of machine learning and deep learning, it highlights its various types and unique capabilities within disaster scenarios. Organized around the three stages of disaster management, this chapter reviews recent research progress, applications, and case studies across the pre-disaster, during-disaster, and post-disaster stages. Furthermore, it explores the integration of deep learning/machine learning with advanced technologies, addressing challenges such as data quality and ethical considerations, providing insights into future developments of deep learning/machine learning in disaster management.