Background: Due to graphene is currently incorporated into various consumer product and numerous new applications, determining the relationships between physicochemical properties of graphene and their toxicity is a prominent concern for environmental and health risk analysis. Data from the literatures suggested that graphene exposure may resulted in cytotoxicity, however, the toxicity data of graphene is still insufficient to point out its side because of the complexity and heterogeneity of available data on potential risks of graphene. Methods and Results: Here, we developed a meta-analysis approach for assembling published evidence on cytotoxicity based on 792 related publications, 986 cell survival rate samples, 762 IC50 samples, and 100 LDH release samples. In this study, among corresponding attributes, we proved that the cytotoxicity of graphene assessed in the form of cell viability, IC50 and LDH can be primarily predicted from exposure dose and detection method, diameter and surface modification, detection method and organ source, respectively. Furthermore, this paper provides guidance regarding three optional data sets for above-mentioned three endpoints that are chiefly related to cellular toxicity for future studies and cross-validation studies based on machine learning tools including Random Forests (RFs), Support Vector Machine (SVM), LASSO regression, and Elastic Net were conducted for result verification. Conclusions: In summary, our study indicates that following rigorous methodological experimental and extract approaches accompanied with suitable machine learning tools, in parallel to continuous addition to reliable data set developed using our meta-analysis approach, will offer higher predictive power and accuracy, and also help to provide effective information on designing safe graphene.