Ionic liquids have applications across various scientific fields, in part, due to their interesting physical and chemical properties. Comprehensive assessments of their toxicological profiles are necessary to allow their safe and suitable applications. Unlike prior ionic liquid toxicity predictions which rely on small descriptor sets, we integrate joint 2D and 3D descriptors with random forest which explains the most important descriptors to address these limitations. This study aims to predict the toxicity of ionic liquids using 2D and 3D molecular descriptors by utilizing machine learning. In particular, we propose the random forest regression model to uncover molecular descriptors and toxicity patterns. Additionally, GridSearchCV is used to tune the hyperparameters to ensure optimal model performance. Several metrics were calculated to evaluate the model’s accuracy. The model achieved R²=0.879 which indicates strong predictive performance. Our study demonstrates the benefits of 2D and 3D descriptors for predicting the toxicity of ionic liquids, showing strong correlations between experimental and predicted toxicities. Our analysis of features using 2D and 3D descriptors highlighted those descriptors that are strongly associated with toxicity predictions. Feature importance highlights that physicochemical factors effect toxicity which provides interpretation for ionic liquid design. This study demonstrates the effectiveness of predicting the toxicity of ionic liquids by integrating molecular descriptors and machine learning, thereby facilitating the safer production and application of ionic liquids.