This project, developed as part of a data science training program, presents a multiple linear regression model to predict neonatal birth weight. The analysis is based on a clinical dataset of 2,500 cases from three hospitals, incorporating variables such as maternal age, gestational duration, and smoking habits. The objective is to identify the most influential factors to enhance pregnancy management and optimize hospital resources. The project includes exploratory data analysis, optimal model selection, and interactive visualizations to effectively communicate the findings. This work highlights the potential of inferential statistics in healthcare, contributing to improved neonatal care quality.