Transportation supports global connections in the world. Urban planning requires much thought about transportation to ensure connectivity and simplicity within a city. However, what should also be considered is the environmental impact of their decisions. There is variation in how each county approaches its development projects. These differences play a crucial role in their environmental impact. The research aims to understand how different types of transportation infrastructure impact carbon emissions. Each country was categorized by region, and the study looked into six different regions: North America, South America, Asia, Europe, Africa, and Oceania. The ideal features for predicting the carbon emissions per capita were determined using random forest regression, a machine-learning model that creates many decision trees to make predictions. The carbon emissions per capita with the percentage of roads paved, the feature deemed most valuable to predicting carbon emissions per capita by the random forest regressor, in each region was compared. With linear regression, a relationship was determined in each region to understand the variance from the line and the slope of the line and to know how the data progresses as the values increase. The results showed that economic and governmental factors were often at the root of environmental factors, and transportation infrastructure reflects economic strength. This was understood from both correlations between carbon emission changes and financial events, as well as from policy reviews of countries that were anomalies in the data, showing that they had differing policies regarding transportation. In this research, transportation infrastructure was proven essential to environmental factors. The study also indicates that the jobs of urban planners and road workers play a significant role in how a country approaches its carbon-neutral goals.