The persistent challenge of nutritional inequity in low-income communities demands innovative, evidence-based intervention frameworks that leverage contemporary data analytics capabilities. This study presents a comprehensive framework for developing and implementing data-driven nutrition interventions specifically designed for high-risk, low-income populations across diverse geographic contexts. The framework integrates multiple analytical methodologies, including predictive modeling, geospatial analysis, community participatory approaches, and real-time monitoring systems to address systemic barriers to nutritional access and health equity. Drawing from extensive literature on community health interventions (Kingsley et al., 2020), chronic disease management (Stellefson et al., 2013), and data analytics applications in healthcare (Nwaimo et al., 2019), this research establishes a structured methodology for identifying vulnerable populations, assessing nutritional risks, and designing culturally appropriate interventions. The framework incorporates six key analytical domains: community needs assessment and risk stratification, intervention design and resource allocation, implementation pathway optimization, monitoring and evaluation mechanisms, sustainability and scalability considerations, and continuous quality improvement processes. Particular emphasis is placed on addressing social determinants of health, including food insecurity, environmental contamination (Onyekachi et al., 2020), healthcare access barriers, and systemic inequities that disproportionately affect marginalized communities (Geronimus et al., 2020). The proposed framework demonstrates applicability across urban and rural settings, accommodates diverse cultural contexts, and provides actionable guidance for public health practitioners, policymakers, and community organizations. By integrating machine learning forecasting algorithms (Fasasi et al., 2020), predictive analytics (Abass et al., 2019), and community engagement principles (Wallerstein et al., 2015), this framework offers a replicable model for reducing nutritional disparities and improving health outcomes in underserved populations. The research contributes to emerging scholarship on precision public health, health equity interventions, and data-driven decision-making in community health programming, providing both theoretical foundations and practical implementation strategies for addressing one of the most pressing public health challenges of contemporary society.