Healthcare disparities affecting immigrant populations in the United States continue to present significant public policy challenges, particularly in the context of Medicaid expansion and access equity. Immigrants—especially those with undocumented status or limited English proficiency—face compounded barriers due to restrictive eligibility criteria, administrative complexity, and fragmented care coordination. This study argues that health data analytics can serve as a transformative tool to inform and advocate for more inclusive Medicaid expansion and comprehensive immigrant healthcare policy reform. The research begins by examining existing disparities in coverage, utilization, and outcomes across immigrant subgroups using national datasets such as the Medical Expenditure Panel Survey (MEPS), American Community Survey (ACS), and Medicaid claims data. Through advanced predictive modeling and geospatial analysis, the paper identifies underinsured immigrant communities at elevated risk for chronic disease burden, preventable hospitalizations, and economic exclusion. Moving from insight to impact, the study proposes an analytical framework that integrates social determinants of health (SDOH), linguistic isolation metrics, and policy simulation tools to assess the cost-benefit of expanding Medicaid eligibility for non-citizen residents. Case examples from states with inclusive Medicaid policies (e.g., California, New York) illustrate how data-informed interventions can reduce uncompensated care costs, improve health equity, and increase public health system efficiency. Finally, the paper outlines policy recommendations for leveraging health data ecosystems, including interoperable health information exchanges (HIEs), to support real-time monitoring and accountability in Medicaid reform efforts. By grounding policy innovation in rigorous analytics, this research positions evidence-based advocacy as central to shaping an equitable and sustainable healthcare future for immigrant communities.