Maternal Healthcare Monitoring Using Artificial Intelligence and Internet of Things: A Comprehensive Review

Izza Izzany Mohd Idris; Nur Ilyani Ramli; Ezak Fadzrin Ahmad Shaubari; Azizul Fahmi Aziz

doi:10.56532/mjsat.v6iS2.818

Authors

Izza Izzany Mohd Idris Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia
Nur Ilyani Ramli Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia
Ezak Fadzrin Ahmad Shaubari Faculty of Computer Science and Information, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia
Azizul Fahmi Aziz Quantum Medical Solution, G-05, Pangsapuri Servis Meritus, Jalan Laguna 1, 13700, Perai, Pulau Pinang, Malaysia

DOI:

https://doi.org/10.56532/mjsat.v6iS2.818

Keywords:

Maternal, Monitoring, Artificial Intelligence (AI), Internet of Thing (IoT)

Abstract

Maternal healthcare monitoring has emerged as a critical research domain due to the persistent challenges of maternal and fetal morbidity, limited access to continuous prenatal care, and the growing demand for timely, data-driven clinical decision-making. Recent advances in Artificial Intelligence (AI) and the Internet of Things (IoT) have enabled intelligent, real-time, and non-invasive maternal monitoring solutions, particularly for high-risk and resource-constrained settings. This systematic literature review aims to comprehensively examine state of the art AI and IoT based maternal healthcare monitoring systems, identify prevailing research themes, and highlight technological trends and gaps. The review was conducted following the PRISMA protocol, utilizing two major scientific databases Web of Science and Scopus. An advanced search strategy employing the keywords “maternal,” “monitoring,” “AI,” and “IoT” was applied, with studies limited to publications between 2018 and 2025. After identification, screening, eligibility assessment, and quality evaluation, a total of 26 primary studies were included for in depth analysis. The findings were systematically classified into three dominant themes: (1) IoT based wearable sensing for maternal fetal monitoring, focusing on real time physiological data acquisition; (2) AI driven prediction and decision support in smart maternal care, emphasizing machine learning models for risk assessment and early complication detection; and (3) remote prenatal care platforms, system architectures, and rural health enablement, highlighting telemedicine-oriented solutions for underserved populations. Numerical analysis indicates a substantial increase in integrated AI IoT frameworks, with over half of the studies proposing predictive or decision-support capabilities. Despite notable progress, challenges remain regarding data reliability, interoperability, scalability, and clinical validation. This review provides structured insights into current research directions and serves as a foundation for future developments toward reliable, intelligent, and accessible maternal healthcare monitoring systems.

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