Thus, integrating data analysis and ML-based predictive modeling emerges as a strategic approach to accelerating the discovery and optimization process. Machine learning techniques, such as k-nearest neighbors (KNN) and random forest (RF), have been widely employed to identify patterns and predict the impact of key variables on experimental outcomes. This approach enables the rapid screening of synthesis conditions and the prioritization of experiments, saving time and resources .