This review discusses computational nanotoxicology models for assessing the environmental risks of engineered nanomaterials (ENMs). ENMs, which are nanoscale materials with unique properties, pose potential risks to human health and the environment. Traditional risk assessment methods, such as experimental testing, are time-consuming and impractical for evaluating the large number of ENMs. In contrast, computational methods, including in silico models, have gained attention for their efficiency and ability to predict ENM behavior. The review highlights key computational nanotoxicology models, including material flow analysis (MFA), multimedia environmental models (MEM), physiologically based toxicokinetics (PBTK), quantitative nanostructure–activity relationships (QNAR), and meta-analysis. These models help assess ENM exposure and hazards by simulating their fate, transport, and interactions in the environment and within organisms. MFA models track the flow of ENMs through various environmental and technological compartments, while MEM models simulate their environmental fate and transport based on mechanistic processes. PBTK models predict the internal exposure of ENMs in organisms, considering their absorption, distribution, metabolism, and excretion. QNAR models use nanostructure descriptors to predict ENM toxicity, while meta-analysis integrates data from multiple studies to improve model accuracy and extrapolation capabilities. Despite these advances, challenges remain, including limited parameters for environmental fate modeling, the need for more detailed input data, and the difficulty in predicting ENM behavior due to their unique physicochemical properties. Additionally, QNAR models require high-quality data and nanodescriptors, which are currently limited. Future research should focus on improving model accuracy, incorporating diverse exposure routes, and developing more comprehensive databases for ENMs. Computational nanotoxicology models are essential for assessing the environmental risks of ENMs and ensuring their safe use.This review discusses computational nanotoxicology models for assessing the environmental risks of engineered nanomaterials (ENMs). ENMs, which are nanoscale materials with unique properties, pose potential risks to human health and the environment. Traditional risk assessment methods, such as experimental testing, are time-consuming and impractical for evaluating the large number of ENMs. In contrast, computational methods, including in silico models, have gained attention for their efficiency and ability to predict ENM behavior. The review highlights key computational nanotoxicology models, including material flow analysis (MFA), multimedia environmental models (MEM), physiologically based toxicokinetics (PBTK), quantitative nanostructure–activity relationships (QNAR), and meta-analysis. These models help assess ENM exposure and hazards by simulating their fate, transport, and interactions in the environment and within organisms. MFA models track the flow of ENMs through various environmental and technological compartments, while MEM models simulate their environmental fate and transport based on mechanistic processes. PBTK models predict the internal exposure of ENMs in organisms, considering their absorption, distribution, metabolism, and excretion. QNAR models use nanostructure descriptors to predict ENM toxicity, while meta-analysis integrates data from multiple studies to improve model accuracy and extrapolation capabilities. Despite these advances, challenges remain, including limited parameters for environmental fate modeling, the need for more detailed input data, and the difficulty in predicting ENM behavior due to their unique physicochemical properties. Additionally, QNAR models require high-quality data and nanodescriptors, which are currently limited. Future research should focus on improving model accuracy, incorporating diverse exposure routes, and developing more comprehensive databases for ENMs. Computational nanotoxicology models are essential for assessing the environmental risks of ENMs and ensuring their safe use.