<div>Truck platooning facilitates the operation of trucks in close proximity to one another, resulting in decreased air resistance and improved fuel efficiency. While previous research has mostly focused on the effects of intra-distance on fuel savings, this study aims to develop fuel savings performance functions considering various truck platooning configurations. This article comprehensively investigates the influence of different truck platoon configurations on fuel savings. This analysis focuses on examining the impacts of several variables including inter-vehicle distance, platoon speed, truck weight, number of trucks in the platoon, and the truck’s distinctive design characteristics. Data used in the analysis were collected from 10 different field experiments. Three machine learning techniques—artificial neural networks (ANN), extreme gradient boosting (XGBoost), and K-nearest neighbors (KNN)—alongside the negative binomial regression model were employed. Upon evaluation, the negative binomial regression model emerged as the most accurate, boasting a prediction accuracy of 74%. This high-performing model was subsequently leveraged to derive an equation for estimating fuel savings. The results indicated that the truck platoon’s size is the most significant factor affecting fuel efficiency. Specifically, the inclusion of additional trucks in the platoon leads to substantial fuel savings. Moreover, as the platoon’s speed increases, there is a noticeable increase in fuel savings. The design of the truck plays a role: conventional trucks are more fuel efficient than cab-over trucks. Lastly, the weight of the truck has a minor impact on the platoon’s fuel efficiency. Overall, it is essential to consider multiple variables when evaluating truck platoon arrangements for optimal fuel efficiency.</div>