In this paper we introduce a two-staged pipeline to tackle the problem of bike redistribution for bike-sharing systems, using Automated Machine Learning (AutoML) and optimization techniques. Our approach includes the usage of AutoML for time series forecasting in order to estimate the demand for bikes for each station, along with an optimization model to efficiently relocate bikes to maximize user satisfaction. In our study, we used historical data from Barcelona’s public bike-sharing system to predict future demand and then used these predictions together with public data from OpenStreetMap (estimated travel time between stations) in order to solve the Minimum Cost Flow Problem (MCFP) and compute the optimal bike redistribution. We demonstrate promising results in terms of accuracy of demand forecasting and reduction of forecasting time, thus obtaining feasible redistribution strategies and providing an end-to-end framework to the operator.