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Effective decision-making in emergency response and public health resource allocation is critical for mitigating risks, optimizing resource distribution, and ensuring timely interventions during crises. Traditional decision-support systems (DSS) have evolved with advancements in artificial intelligence (AI), machine learning (ML), and big data analytics, enhancing real-time predictive capabilities and resource optimization. This paper explores recent developments in decision-support frameworks that integrate data-driven methodologies for improved situational awareness, response coordination, and resource allocation. Modern DSS leverage geospatial information systems (GIS), agent-based modeling, and deep learning algorithms to analyze large-scale epidemiological, demographic, and logistical datasets. These technologies facilitate dynamic risk assessment, scenario planning, and adaptive resource deployment. Additionally, cloud computing and Internet of Things (IoT) devices enhance data collection and processing speeds, enabling faster decision-making during emergencies such as pandemics, natural disasters, and bioterrorism threats. A critical challenge in emergency decision-support frameworks is balancing equity, efficiency, and scalability. AI-driven decision models incorporate multi-objective optimization techniques, ensuring fair distribution of medical supplies, personnel, and critical care facilities. Moreover, hybrid models integrating reinforcement learning and Bayesian inference improve predictive accuracy and robustness against uncertainties. This study reviews case applications of AI-powered DSS in pandemic response, hospital surge capacity management, and vaccine distribution. Key lessons from COVID-19 illustrate how predictive analytics and real-time dashboards enhanced public health responses. Ethical considerations, including data privacy, algorithmic bias, and the need for transparent decision-making, are also discussed. Future research should focus on integrating explainable AI (XAI) into DSS to enhance interpretability and stakeholder trust. Additionally, blockchain technology holds promise for securing data integrity in decentralized emergency management systems. By advancing decision-support frameworks, policymakers and healthcare practitioners can develop resilient, adaptive, and efficient emergency response strategies to safeguard public health.