The increasing frequency and intensity of climate disasters necessitate more effective management strategies, and artificial intelligence (AI) and machine learning (ML) offer promising solutions. There is a need to explore the challenges and future trends in applying AI and ML for climate disaster management. Key challenges include data quality and availability, where incomplete or inconsistent data can hinder model accuracy. Computational requirements cause hurdles to the AI and ML approaches, as they require substantial processing power and energy consumption. Model interpretability and transparency are critical issues for AI-driven systems as most of the approaches function like “black boxes,” which makes it difficult to validate predictions and build trust. Ethical and social considerations like safeguarding equity and fairness, also play a significant role in the responsible deployment of these technologies. Despite these challenges, several trends highlight a huge potential for AI and ML in climate disaster management. Enhanced data integration and processing capabilities will enable more comprehensive analysis for better-informed decision-making. The development of higher-accuracy climate models and real-time monitoring systems will improve predictive capabilities for more effective early warning systems and impact assessments. The establishment of regulatory and ethical frameworks will guide the responsible development and application of AI and ML in this field. The integration of AI and ML in climate disaster management has significant opportunities to enhance predictive models and support mitigation and adaptation efforts for a resilient and sustainable future.