Specifically, a Gaussian process was used to predict the titration curves of several mixtures and guide the pH adjustment towards a set target pH. Chemical inputs were featurized containing increasing levels of chemical information, delivering only marginally better efficiency. This can be regarded as advantageous since it allows to implement this approach for systems without the requirement of molecular information, particularly beneficial when dealing with confidential industrial formulation samples or when the composition of the sample has not yet been characterized in detail. Applying transfer learning to the optimization cycle significantly boosted the performance, thus highlighting the main advantage of MLdriven pH adjustment over PID controlled or manual pH adjustment. Since it is common for samples in high-throughput formulation preparation to differ in only one or a few parameters in their compositions, learning from previous pH adjustments and transferring the obtained system knowledge into a new pH adjustment process has been quantitatively shown to benefit the overall workflow.