###### Abstract
In a different study, the researchers calculated that an e-scooter vehicle with 1 kW/h energy travels about 100 km, and an electric vehicle with the same amount of energy travels 6 km [24]. In other words, electric micro-mobility vehicles are more advantageous in energy and cost than traditional and conventional electric vehicles [15]. Some studies have observed that micro-mobility delivery vehicles, such as e-scooters, provide more advantages than traditional vehicles in terms of energy, time, cost, and environmental factors. According to a study evaluating many factors such as time, cost, and environment for conventional and micro-mobility delivery vehicles, diesel fuel energy vehicles have higher emissions and distribution costs than micro-mobility vehicles using electrical energy. The same study emphasized that traditional distribution vehicles (Payload 6500-7900 L and 710-1490 kg for Van) have a higher load than electric micro-mobility vehicles (Payload, 160-300 L and 170-210 kg for Cargo-Bike, 160-300 L and 170-200 kg for E-Cargo-Bike, 100-200 L and 180-250 kg for E-Scooter) and that conventional distribution vehicles have ease of use compared to micro-mobility vehicles [25]. Although the input parameters differ in the literature studies, the same variables have been used in many studies. Mainly environmental factors have been used in most studies. The most important feature that distinguishes this study from other studies is that this study analysed how micro-mobility vehicles, specifically e-scooter vehicles, are affected by input variables. The factors used in other studies generally express the effects of input parameters on micro-mobility vehicles such as e-scooters used for transportation. The effects of micro-mobility vehicles on energy, cost, and environmental factors in package or mail delivery were analysed with many sub-basic indicators. Numerical results were obtained by using different methods for these analyses. In this study, we preferred ML algorithms for time and energy cost estimations using e-scooter vehicles for package or mail delivery. ML algorithms are more potent than traditional statistical methods for predictive data. Unlike conventional statistical methods, ML algorithms enable the formation of predictive data by recognizing the data with algorithms developed in the computer environment and discovering the standard features between the data. Before obtaining the prediction data, ML algorithms divide the existing data into a specific ratio and form the training and training phases. ML algorithms, which learn the properties of the training data, make predictions on the training data [26]. The accuracy of the forecast data is measured with the values of the performance criteria, and the values of the forecast data are confirmed [27]. Detailed information about the features of this study that makes it different from other studies is shown in Table 1. The common aspect of these studies is that they realize different purposes of e-scooter use by using different ML algorithms. By revealing the differences between the parameters used in this study and the variables used in the other study, the effects of different ML algorithms on the e-scooter vehicles used for various purposes were examined. ML algorithms have been developed for delivery time and energy cost estimation in e-scooter use, considering environmental, operational, date, demographic information of drivers, and geographical factors. The present study considers that 15 parameters under