There is an urgent need to develop predictive methodologies that will fast-track the industrial implementation of organic solvent nanofiltration (OSN). However, the performance prediction of OSN membranes has been a daunting and challenging task, due to the high number of possible solvents and the complex relationship between solvent-membrane, solute-solvent, and solute-membrane interactions. Therefore, instead of developing fundamental mathematical equations, we have broken away from conventions by compiling a large dataset and building artificial intelligence (AI) based predictive models for both rejection and permeance, based on a collected dataset containing 38,430 datapoints with more than 18 dimensions (parameters). To elucidate the important parameters that affect membrane performance, we have carried out a thorough principal component analysis (PCA), which revealed that the factors affecting both permeance and rejection are surprisingly similar. We have trained three different AI models (artificial neural network, support vector machine, random forest) that predicted the membrane performance with unprecedented accuracy, as high as 98% (permeance) and 91% (rejection). Our findings pave the way towards appropriate data standardization, not only for performance prediction, but also for better membrane design and development.