This review discusses the progress and challenges in computational nanotoxicology models for assessing the environmental risks of engineered nanomaterials (ENMs). Traditional risk assessment methods, such as environmental field monitoring and animal-based toxicity testing, are time-consuming and expensive, making them impractical for evaluating the increasing number of ENMs. Computational methods, including material flow analysis (MFA), multimedia environmental models (MEM), physiologically based toxicokinetics (PBTK) models, quantitative nanostructure-activity relationships (QNAR), and meta-analysis, have gained attention due to their efficiency and accuracy. MFA and MEM models predict the fate and environmental concentrations of ENMs, while PBTK models estimate internal exposure concentrations. QNAR models use machine learning algorithms to predict the toxicity of ENMs based on their physicochemical properties. Meta-analysis integrates data from multiple studies to generate comprehensive datasets for QNAR modeling. Despite these advancements, challenges remain, such as limited parameterization in MFA and MEM, the need for new PBTK models considering different exposure routes, and the lack of high-quality data for QNAR models. The review highlights the importance of addressing these challenges to enhance the reliability and applicability of computational nanotoxicology models in environmental risk assessment.This review discusses the progress and challenges in computational nanotoxicology models for assessing the environmental risks of engineered nanomaterials (ENMs). Traditional risk assessment methods, such as environmental field monitoring and animal-based toxicity testing, are time-consuming and expensive, making them impractical for evaluating the increasing number of ENMs. Computational methods, including material flow analysis (MFA), multimedia environmental models (MEM), physiologically based toxicokinetics (PBTK) models, quantitative nanostructure-activity relationships (QNAR), and meta-analysis, have gained attention due to their efficiency and accuracy. MFA and MEM models predict the fate and environmental concentrations of ENMs, while PBTK models estimate internal exposure concentrations. QNAR models use machine learning algorithms to predict the toxicity of ENMs based on their physicochemical properties. Meta-analysis integrates data from multiple studies to generate comprehensive datasets for QNAR modeling. Despite these advancements, challenges remain, such as limited parameterization in MFA and MEM, the need for new PBTK models considering different exposure routes, and the lack of high-quality data for QNAR models. The review highlights the importance of addressing these challenges to enhance the reliability and applicability of computational nanotoxicology models in environmental risk assessment.