Health and wells of pregnant women are of paramount importance to reduce morbidity and mortality in mothers and newborns. Limitations of traditional health systems as inconsistent surveillance, rare prenatal visits, and slow detection of pregnancy-related complications pose significant risks, especially in low-resource environments. This study presents a real-time pregnancy health tracking system in real time. It uses the performance of mobile applications integrated into a data-controlled risk assessment model to provide continuous and personalized support from maternal healthcare.This system is designed to allow pregnant women to perform important health parameters for self-monitoring, such as blood pressure, heart rate, weight, blood sugar levels, fetal movement count, and emotional wells. This includes intelligent memory of medication and appointments, educational resources tailored for each mid-pregnancy, and arm characteristics. In the backend, the application uses algorithms for machine learning trained with historical pregnancy data to assess risk levels and predict possible complications such as gestational diabetes, hypertension, and premature babies.This article not only shows mothers' technological innovations, but also highlights the role of real-world data analytics and mobile health (MHealth) in redesigning privacy delivery. The proposed system could bridge the gap between patients and providers, especially in remote and under sector communities, which contributes to global efforts to achieve sustainable development point 3 (good health and wells).
pregnancy, maternal health, mobile health, health tracking system, real-time monitoring, risk assessment, data analytics.
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