In this talk, we explore the application of machine learning techniques to address parameter optimization problems with parabolic PDE constraints. We delve into an established trust region method that integrates reduced basis methods for model order reduction, examining the potential enhancement of this approach through adding machine learning based surrogate models. While open challenges remain, these methods hold promise for providing a more efficient solution to optimization problems in this context.