The development of ML methods to predict the synthesis parameters for a desired MOF crystal structure based on scientific literature is a challenging but promising approach that will advance and accelerate chemical synthesis. Over the last years, ML methods have rapidly evolved, solving complex problems that involve highly nonlinear or massively combinatorial processes that conventional approaches fail to answer. Up till now, ML approaches have been successfully applied to address challenges in organic and inorganic synthesis. In the case of MOF synthesis, only recently, ML was used to optimize synthesis parameters for HKUST-1 and to determine the importance of the different parameters by analysing a set of partially failed experiments, in other words, "capture the chemical intuition" that can help to speed up the synthesis of similar MOF systems. However, the inverse synthesis design of MOFs, i.e. the automated prediction of suitable synthesis conditions for a targeted MOF structure (e.g. designed in silico) remains an unsolved challenge. This work represents a first step towards predicting synthesis conditions for an arbitrary MOF. We show a complete ML workflow for the inverse synthesis design of MOFs (going from crystal structure to synthesis conditions), (1) starting from automated data mining from scientific literature on MOF synthesis conditions and their structural information, (2) setting up and training of ML models, and (3) prediction of synthesis conditions for new MOF structures and comparison with human experts' predictions.