The lack of machine readable and curated MOF-synthesis data up till now hindered the development of digital ML tools for predicting MOF synthesis conditions. Here, we established a SynMOF database by automatic data extraction via NLP methods that provides synthesis conditions and structural information for more than 900 MOFs, and trained ML models based on these data to identify patterns in MOF synthesis. We expect that the created SynMOF database will boost the NLP research within the MOF community, while our ML synthesis prediction platform will be the new gold standard for data-driven MOF discovery. Even at an initial stage, our ML models outperformed MOF experts' synthesis prediction, underlying both the complexity behind the synthesis process and a pressing need in developing digital predictive tools. Our automated on-demand synthesis prediction will considerably accelerate the discovery of new MOFs and serve a valuable tool for the MOF community and beyond.