Nanofiltration is one of the core technologies to address water crises and ensure water quality safety and security. However, the performance of nanofiltration membranes has long been limited by the permeance/selectivity trade-off. Thus, developing high-performance nanofiltration membranes is an urgent need. Nanofiltration membrane fabrication involves factors such as aqueous phase monomer concentration, aqueous phase additive concentration, oil phase monomer concentration, polymerization time, etc. Traditional trial-and-error experimental method consumes substantial manpower, material and financial resources. In this study, based on the fabrication parameters of nanofiltration membranes, we constructed a machine learning-based predictive screening model for nanofiltration membranes. The results show that the XGBoost machine learning model effectively predict the permeance and rejection performance of nanofiltration membranes, with R² evaluation scores of 0.84 and 0.90, respectively, for the permeance and rejection performance. The quantitative analysis of the input parameters in the XGBoost machine learning model using the SHAP value method reveals that the aqueous-phase monomer concentration and the substrate type had high average SHAP values of 2.77 and 2.59 for permeance. The average SHAP values of the key parameters oriented towards the rejection performance were relatively close. The results of the monomer substructure characterization show that the hydrophilic and branched substructure features contribute to the permeance, while the amine group promote the rejection performance. The nanofiltration membrane prediction and screening model can identify and optimize the key parameters, providing theoretical and technical support for developing nanofiltration membranes.