Toxicity profiling of specialty chemicals is essential, since several studies have reported their role in acute/chronic health effects. It is voluminous to perform a battery of toxicity experiments on available specialty chemicals. In this study, we employed robust QSAR approaches to predict the carcinogenicity and mutagenicity potential for a dataset of 131 specialty chemicals utilizing machine learning tools. Four predictive approaches were selected to benchmark the reliability and applicability of the suitable genotoxicity QSAR (Geno-QSAR) models each for carcinogenicity (CAESAR, ISS, ANTARES, and ISSCAN) and mutagenicity (CAESAR, SARpy, ISS, and KNN). Five-fold statistical evaluation was performed using an external dataset of more than 2000 compounds with their known genotoxicity potential. KNN/Read across and IRFMN/ANTARES resulted as the best model for mutagenicity and carcinogenicity, respectively. Results obtained from the selected predictive models are narrowed down to the potentially safe compounds and are cross-validated with the experimental details compiled through the literature mining. Geno-QSAR approaches demonstrated in this investigation have widespread applicability for safe compound prioritization and toxicity prediction of a large number of chemicals in a lucid way.