This report presents a statistical analysis of the impact of key maternal characteristics, including age, smoking status, parity, height, weight, and gestation period, on newborn birth weight. A real- world dataset comprising 1,236 observations was utilized for this investigation. The methodology involved comprehensive data cleaning, exploratory data analysis (EDA), and a series of parametric statistical tests, specifically the One-Sample t-test, Two-Sample t-test, Chi-Square tests, and Analysis of Variance (ANOVA). All analyses were conducted within the SAS programming environment. The study’s findings indicate a statistically significant negative impact of maternal smoking on birth weight, a finding consistent with broader public health literature. Gestation period emerged as the strongest positive predictor of birth weight within this dataset. While the analyses using broad categories of maternal age and parity did not reveal significant differences in mean birth weight, a review of existing literature suggests more intricate, potentially non-linear relationships and nuanced effects of these factors. Similarly, maternal pre-pregnancy weight, though showing a weak linear correlation in this dataset, is widely recognized as a critical determinant of birth weight outcomes, particularly at its extremes. These results emphasize the importance of targeted prenatal care interventions, especially those focused on smoking cessation. The study reinforces the utility of data-driven insights in informing public health policies aimed at improving maternal and child health outcomes. Future research should explore non-linear relationships and potential interactions among various maternal factors.