Micro-mobility refers to a variety of small and lightweight vehicles designed for individual use. These new vehicles are inexpensive, simple to operate, and enjoyable to ride, making them the easiest and most suitable mode of transportation for trips of less than five miles. They have become extremely popular, especially with the advent of free-floating systems that offer users flexible parking in order to facilitate the rental process. However, the problem of imbalance and maldistribution is among the major challenges of these systems, causing dissatisfaction and loss of customers. Therefore, to ensure the balancing of the fleet and to make the best decision for its reorganization, we must consider strategic locations that are accessible to all. In this paper, we propose a machine learning model for spatio-temporal demand forecasting using a multi-output regression technique. The main goal of the paper is to help pick the ideal areas for fleet deployment and balance the system according to user needs. Our solution, designed for public electric scooters, is based on the estimation of user demand over a grid-based service area. In addition, we propose an enhanced solution that outperforms other baseline models, including the Random Forest, Gradient Boosting, and Stacking Regressor.