Renewable Energy Source Implementation Alongside Predictive Analysis Methods Works Together to Boost Maternal and Newborn Healthcare Outcomes in Underprivileged Population Areas of America

Public health officials recognize maternal and neonatal health disparities as a significant problem that most strongly affects underserved populations because they lack adequate medical care and face intermittent power supply challenges that diminish patient recovery potential. High preterm birth rates affect the health of both mother and child particularly within the mother demographics of women who are 36 years or older. The combination of bad healthcare facilities and insufficient energy supply blocks the deployment of vital medical instruments which drives risks against newborns’ health along with their mothers’. A scientific analysis examines how renewable energy systems and predictive analysis work together to enhance healthcare results among risk-bearing maternity patients. The research study evaluates how renewable energy technologies, particularly solar power systems work to boost the reliability of maternal healthcare delivery services. Predictive analytics along with machine learning models serve as exploration methods to detect dangerous pregnancies and stop bad outcomes from happening in maternal and newborn health. The study blends quantitative methods including hospital files and energy performance measures with predictive model precision and qualitative data acquired through health professional and patient interviews to achieve its objectives. Data will be collected through surveys, case studies, and AI-driven predictive modeling. The expected findings will highlight how integrating renewable energy into healthcare facilities can improve service delivery while AI-driven predictive analytics can enhance early detection of maternal health risks. These insights will support policymakers, healthcare providers, and researchers in developing sustainable, data-driven solutions to reduce maternal and neonatal mortality in underserved communities.