All these properties for 11 solvent types were extracted from PubChem. Predictive performances of different Artificial Neural Network (ANN) architectures; a back-propagation neural network (BPNN) and a general regression neural network (GRNN) were compared. ANN consists of an input (independent variable), output (dependent variable) and a hidden layer (function). Each neuron in hidden layer is connected to all other neurons in the form of weights. The modeling process includes the training and testing of the model. All data were firstly shuffled and then split into the training and testing sets. The training sets were used for data fitting which is the process of constructing a mathematical function/model with the best fit to a series of data points. A well-fitted model produces more accurate outcomes. Once the ML model is considered trained, its performance can be assessed by a test set, which consists of a smaller sample in comparison to the training set. Two main problems might arise during testing: (i) if the descriptor vectors present an insufficient number of features, the model is said to be underfitted and doesn't match the data closely enough, and (ii) if the descriptor presents too much information, the model is overfitted and it matches the data too closely. As a sensitivity test, multiple training and testing processes were repeated with randomly shuffled data before each modeling. Then to evaluate the accuracy of the model the average root-mean-squared error (RMSE) of the testing set was also acquired. Lower average RMSE of the model indicates a model with better predictive accuracy, such as the single hidden layer model (red data). Two factors directly affected the accuracy of the models: i) the number of neurons in the hidden layer and, ii) the size of the training set. A greater number of neurons and a larger training test ensured higher accuracy of the model. It is also observed that BPNN had better extrapolation predictive capacity than GRNN since GRNN is mainly designed for interpolation with a kernel-based algorithm. In order to compare the linear and nonlinear fitting results, multiple linear regressions (MLR) were also performed and compared with ANNs. Results showed that Methanol gave the highest Co2 solubility of 0.09-0.88 mole fractions at 1.5-7.433 MPa and 278.15-308.15 K.