This study explores the application of supervised machine learning techniques, specifically Poisson and negative binomial regression models, for analyzing count data to forecast outgoing mail volume for the General Directorate of Posts of Saudi Arabia from 2002 to 2006. The dataset covers 13 administrative regions and consists of 65 observations with 3 variables - the dependent variable is the number of outgoing mails, and the independent variables are year and region. Exploratory data analysis revealed significan…