Abstract

Maternal mortality remains a critical public health challenge in developing health systems, with approximately 295,000 women dying annually from preventable pregnancy-related complications. This comprehensive review examines predictive models and frameworks designed to reduce maternal mortality in low- and middle-income countries through data-driven interventions, community engagement, and health system strengthening. The study synthesizes the intersection of predictive analytics, environmental health assessment, health information systems, community-based interventions, and socioeconomic determinants of maternal health outcomes. Key findings reveal that maternal mortality is influenced by multiple interconnected factors including inadequate access to quality healthcare services, socioeconomic inequalities, nutritional deficiencies, environmental hazards, and weak health information governance. Predictive models incorporating machine learning algorithms, risk stratification frameworks, and geographic information systems demonstrate significant potential for early identification of high-risk pregnancies and targeted resource allocation. Community-oriented primary care approaches, when combined with robust health data protection frameworks and cross-functional compliance mechanisms, enhance the effectiveness of maternal health interventions. The review identifies persistent challenges including limited technological infrastructure, workforce capacity constraints, cultural barriers, and financial resource scarcity that impede the implementation of predictive models in resource-constrained settings. Best practices emphasize the integration of participatory research methodologies, multi-sectoral collaboration, AI-driven workforce forecasting, and culturally sensitive intervention designs. The synthesis demonstrates that successful maternal mortality reduction requires comprehensive frameworks that address both immediate clinical risks and underlying social determinants of health. This review contributes to the growing body of evidence supporting data-driven approaches to maternal health improvement and provides actionable recommendations for policymakers, healthcare administrators, and public health practitioners working to achieve Sustainable Development Goal targets for maternal mortality reduction in developing health systems.