Chemical absorption of CO2 with aqueous amine-based solvents is considered as one of the current benchmark post-combustion capture technologies mainly because of its high process efficiency. When amines are used to capture CO2 from flue gases, changes in the physical and chemical properties of the solvent may take place over time. The changes in these properties of the amines are manifestations of physical changes such as the differential evaporation of components of the aqueous amine solvents and chemical changes resulting from degradation of the amines. Therefore, if changes of these properties relative to the original solvent properties are monitored during the capture process, they could be used to determine whether a physical change such as water evaporation has taken place or a chemical change such as degradation has occurred or both. The amine concentration can be restored by adding the right amount of the right component to restore the amine solution to its original form. This work uses a diagnostic approach to determine the occurrence of physical or chemical changes in amine-based solvents by means of simple solvent property measurements as a way of monitoring solvent quality during the CO2 capture process to maximize the capture efficiency and improve the capture performance of amine-based solvents. In the first phase of the work, amine was prepared at different concentrations to closely imitate the physical changes that could take place in a conventional CO2 capture plant as a result of the differential evaporation of the components of the amine. CO2 loading and temperature were also varied to take into account the different conditions that might exist in the capture plant at the time a sample was taken for analysis. The amine properties at these different concentrations and conditions were measured and then compared to the properties of the original or desired solvent. For the chemical change experiment, the amine was synthetically degraded by intentionally adding different concentrations of primary degradation products and measuring the corresponding properties of the degraded amine. The experimental data obtained from the first phase of the work was then used to develop predictive models that were capable of predicting physical or chemical changes or both based on the change in properties of the amine sample. Three predictive models were developed for physical change using MS Excel, Minitab software and Artificial Neural Network (ANN) with MATLAB software. The standard error for the model developed using MS Excel was determined to be 0.097 with R2 value of 0.97 and average absolute deviation (AAD) of 1.56% between model predicted and actual response values of the validation dataset. The model trained using Minitab reserved a fraction of 0.3 of the data for internal testing and returned standard error of 0.09 and 0.11 for training and test set with R2 values of 0.977 and 0.953 respectively. The AAD between model predicted and actual response values of additional test data was determined to be 1.39%. The trained ANN also returned root mean squared error (RMSE) of the training, validation and test dataset as 0.035, 0.046 and 0.068 respectively with R2 value of 0.995 for the test set. AAD for validation and test set was computed to be 0.91% and 1.47% respectively. An optimizable Gaussian Process Regression (GPR) model was developed for chemical change using Regression Learner tool in MATLAB. The trained model with the best hyperparameters returned R2 values of 0.99 and 0.95 for the validation and test set with RMSE of 0.042 and 0.090 respectively. The AAD for the test set parity plot was computed to be 3.45%. The models with satisfactory performance and accuracy were then selected and employed in the development of an interactive graphical user interface (GUI) to make predictions, interpret predicted values for the user and suggest actions that would enable the user offset any disturbance or deviation from set values.