We developed, optimized, validated, and tested a random forest regression model using the curated descriptors (independent variables) and the experimentally defined LC50 values (dependent variable). Random forest is a decision tree based algorithm where several bootstrap data (i.e. training set) are given to several decision trees. This assures that the dataset given to each tree is unique. Once the model is developed, the most common decision tree model outcome is considered as the random forest model prediction. The main advantage of the random forest modeling strategy is the ability to handle non-linearity and non-continuity in the data, which is highly relevant to toxicity prediction. Here, the acute fish toxicity dataset was divided into training set (90% of the full dataset) and test set (10%). The training set was used for the model development and optimization while the test set was for further evaluation of the dataset. For the regression model, the model hyper-parameter optimization was performed with a two dimensional grid with the number of trees ranging from 100 -1000 whereas the minimum number of points in each leaf varying from 1 -21.