The development of a sap flow prediction model that covers the lack of transpiration measurement information is highly practical. Howev er, a large scale spatial model for forecasting plant sap flow has not yet emerged The purpose of this thesis is to use the LSTM model to pred ict sap flow from the European data sets , using the open data SAPFLUXNET as a source Our database was selected fr om 49 stands containing 618 plant data with 21 input feature s, including 6 dynamic variables and 15 static variables In our results i t was argued that the treatment of missing data could be ignored for now, as it would have little interference with the m odel. In addition, the static variables play ed an important role in extending the model to a large r scale. Plant variables were fundamental to greatly improve t he model, while site and stand variables also ma d e the model robust . Last ly, it was found that i gnoring the extreme values of the sap flow data was unhelpful and that higher sap flows might actually occur. The ultimate model was evaluated by the NSE value with an average of 0.779, at the very good level. T his thesis is expected to be a pioneer on s ap flow prediction using machine learning and open data within a regional or global scale.