As opposed to traditional hyperparameter tuning -where the objective is to find the parameters that deliver a low prediction error -we aimed to decrease the average number of iterations to reach the target pH within the active ML-driven closed-loop optimization. The presented values are the mean (Eqn. We trained ML models on the experimental titration data of the pure chemicals (e.g., ammonium and KH2PO4) and evaluated ML predictions for the binary mixture (experimental data was existent), thus performing an extrapolative prediction. The large feature set, using chemical information was used. Figure illustrates true vs predicted titration curves -as visible the predictions are capable of predicting the trend, however, often clearly miss the ground truth. While we initially attempted to develop a purely predictive model for extrapolation across different systems, we understood the underlying challenges and limitations of accurately modelling the pH of multi-buffered mixtures within extrapolative predictions. Eventually, we changed our strategy from a purely predictive approach to an iterative strategy, involving repeated sampling and model training.