This document summarizes research on using machine learning algorithms to estimate fetal weight at varying gestational ages. The researchers trained models using ultrasound parameters like biparietal diameter, abdominal circumference, femur length as features. They used techniques like SMOTE for imbalanced data and different algorithms like SVM, DBN, and ANN. The goal is to help obstetricians more accurately predict fetal weight compared to traditional ultrasound-based methods, in order to reduce prenatal risks. Literature on similar prior research estimating fetal weight with machine learning from maternal characteristics is also reviewed.