## 4 Conclusions  
In this study, 176 data points from the literature were collected and used to develop ML models for predicting and evaluating the effects of salinity, temperature, and pressure on the DC of CO2 in brine. Three advanced ML models RF, GBR, and XGBoost were selected due to its ability of reducing the chances of overfitting. The data split into 80% for training and 20% for testing. Among these, the RF model demonstrated superior accuracy and efficiency in estimating CO2 DC based on input parameters, significantly reducing the time required compared to laboratory experiments and molecular simulations. The results revealed that temperature is the most influential factor, positively correlating with DC, followed by salinity and pressure, as determined through three different techniques: Tornado chart, SHAP value analysis, and dependency plot. Salinity exhibited a negative correlation with DC across all models, with RF showing the highest accuracy and smallest error (R\({}^{2}\) of 0.96 and RMSE value of 0.03 for the test dataset) in predicting DC values and sensitivity to salinity changes. While the proposed models effectively captured diffusion behavior within the studied parameter range (pressures of 0.1-30 MPa, temperatures of 286.15-398 K, and salinities up to 6 mol/L), their applicability is limited to these conditions. For future work, it would be advisable to use the molecular structure of the salt and use some advanced ML models capable of capturing molecular-level features, which would help to elucidate different mechanisms at the molecular scale. Moreover, there is still a lack of information about the composition of the injected CO2 in most ML models; including these parameters would further improve predictions of the diffusion coefficient. Most importantly, expanding the dataset to include additional experiments and input features such as permeability or porosity could further enhance model performance and robustness. These findings underscore the potential of ML models to provide a robust, data-driven framework for optimizing CO2 capture, storage, and enhanced oil recovery operations by accurately predicting diffusion behavior in brine systems.  
Conceptualization, Q.K., P.P. and F.H.; methodology, Q.K.; software, Q.K.; validation, Q.K., P.P., R.K. and F.H.; formal analysis, Q.K.; investigation, Q.K.; resources, Q.K., P.P. and F.H.; data curation, Q.K.; writing--original draft preparation, Q.K.; writing--review and editing, P.P., F.H. and R.K.; visualization, Q.K. and R.K.; supervision, P.P. and F.H.; project administration, P.P.; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Nazarbayev University under the Faculty Development Competitive Research Grant (Grant No. 201223 FD2608).  
Not applicable.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to express their sincere gratitude to Nazarbayev University for supporting this research through the Faculty Development Competitive Research Grant. We also acknowledge the contributions of previous researchers whose published datasets enabled this study. Special thanks go to the anonymous reviewers for their comments and suggestions, which significantly improved the quality of the manuscript.  
The authors declare no conflict of interest.