This study presents initial results on the integration of machine learning into a sustainability decision support system (MLDSS) designed to enhance organizational sustainability performance management. The proposed framework combines rule-based compliance assessment, ESG scoring, progress measurement, and strategic recommendations to support companies in aligning sustainability goals with operational priorities. A random forest regressor was employed to predict ESG scores from sustainability data extracted from corporate reports, focusing on greenhouse gas emission indicators. Experiments conducted with datasets from 5, 10, and 15 companies demonstrated that model performance improved with larger sample sizes, as reflected in rising R² (0.64 - 0.86) and explained variance scores, alongside reduced error margins. Meanwhile, the RMSE between the actual and model output for the GHGs Emission Score is reduced from 18.58 to 14.6 .The smaller datasets revealed limitations due to imbalance and variability, results from the 15-companies sample size indicated promising predictive capability and underscored the importance of continuous data enrichment. Beyond technical performance, the system’s visualization and tracking tools facilitate transparent stakeholder communication, reinforcing consumer trust and investor confidence. These findings highlight the potential of machine learning–assisted DSS to automate compliance, optimize sustainability strategies, and strengthen ESG reporting, offering a scalable pathway for organizations to advance sustainable practices.