The primary objective of this research is to meticulously examine the factors that significantly elevate the risk of Atherosclerosis Heart Disease (AHD). By employing a Multinomial Logistic Regression model, we analyzed the relationships between the non-binary dependent variable and independent variables such as age, sex, and exercise-induced angina (exang). This predictive analysis not only characterizes the data but also elucidates the connections between the dependent variable and various independent variables, whether they are nominal, ordinal, interval, or ratio-level. The application of logistic regression has become increasingly prevalent in healthcare data analysis. Our study concludes that the type of chest pain and the maximum heart rate achieved are crucial determinants of Atherosclerosis Heart Disease. By comparing chest pain type with independent attributes through the Multinomial Logistic Regression model, we have identified these key attributes, providing valuable insights for better understanding and managing AHD risks.