Authors : Yashvi Ghumre; Priyanka Lamkoti; Shanti Mhatre; Pooja Jambhale

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/yc78sn39

DOI : https://doi.org/10.38124/ijisrt/25may323

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Pregnancy is one of the most fascinating and chal-lenging experiences the human body can go through, and it is different for every person, influenced by a wide range of physical, emotional, lifestyle, and social factors. Early detection and prediction of potential complications are essential to im-proving outcomes for both mother and baby. This project uses various machine learning methods on pre-pregnancy data such as physical health, stress levels, and lifestyle choices to predict the risk of pregnancy-related issues. Further, to support the journey, we developed the Juno application—a digital companion that helps expectant mothers document and manage their pregnancy journey from beginning to postpartum. Users can track physical changes, moods, symptoms, supplement intake, medical appoint-ments, and receive useful reminders and suggestions. The app also provides insights into fetal development and aims to offer support throughout childbirth and after delivery. Juno makes it easier to stay organized, reflect on changes, and recognize when to seek help, contributing to better health outcomes. Our goal is to combine technology with care to offer a simple yet effective tool that supports women through one of the most life-changing Experiences.

Keywords : Pregnancy Monitoring, Maternal Health, Ma-Chine Learning in Healthcare, Risk Prediction, Mobile Health Application.

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