Our approach for active ML-driven closed loop optimization is shown in Figure . A chosen ML model is initially trained within a low data regime (here three datapoints) and used for predicting the unknown (ground truth) full titration curve (Figure ). Subsequently, conditions towards the target pH are selected using a custom acquisition function. In active learning and Bayesian optimization the acquisition function is typically a trade-off between exploration to reduce model uncertainty and exploitation towards the target value. For the pH adjustment we choose a purely exploitative approach by selecting the minimizer of the difference between the model predictions and the target pH as the next experimental condition (Figure ). This is possible as the pH curve is monotonous, hence the algorithm will converge to the target pH. Until the target pH has been reached, the dataset is continuously updated and the model is retrained for the next iteration. We coupled our active ML-guided pH adjustment approach with a liquid handling robot and successfully adjusted a set of chemically different binary buffered mixtures. After a comparison of different surrogate models, we identified Gaussian processes (GP) as the best performing model. Moreover, we managed to boost efficiency of the process by utilizing TL strategies, thus decreasing the required iterations of pH adjustment.