###### Abstract  
This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R\({}^{2}\)) values of RF, GB, NN, and \(k\)NN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R\({}^{2}\) and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm's MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R\({}^{2}\) values of RF, GB, NN, and \(k\)NN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the require the is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient. os 20  
Recently, the development of distribution process concepts has been encouraged to ensure the smooth delivery of distribution services by enabling the application of new technologies due to transportation difficulties [4]. As an example of different and new technology in transportation, micro-mobility has excellent potential to be used for vehicle distribution services [5]. In particular, the preference for micro-mobility vehicles for freight (package, mail, cargo, etc.) and public transportation contributes to economic feasibility in transportation systems and reduces externalities related to urban freight transportation [6]. This means that there are externality links in the vehicle, that is, the use of other transportation infrastructures in freight or goods transportation [7]. This situation harms transportation institutions in terms of cost, time, and environmental parameters throughout the transportation process. The existing literature has shown the success of micro-mobility infrastructure system packet or mail (in limited weight and size) delivery processes for short or specified distance routes. [PERSON] et al. highlight that micro-mobility is increasingly recognized as an urban mode of transport due to its potential to reduce the use of private vehicles for personal needs or large-scale vehicle use for mail or package delivery, with increasing preference for micro-mobility for short-distance travel [8]. A study has shown the positive results of micro-mobility implementation by the Serbian Public Enterprises Post Office to review the process of package or mail deliveries and to evaluate the possibility of applying alternative models [9]. Micro-mobility vehicles began to take place among the essential means of transportation in terms of human mobility because human mobility has been an important factor in the human necessities of life. A study discussed the importance of shared micro-mobility tools in transportation for human mobility and their impact on climate, public amenities, and economic factors [10].