Herein, we aim to employ a data-driven strategy for pH adjustment, benefitting from active learning and robotic facilities for experimental evaluation. We compare different surrogate models, machine-readable data representations and initialization strategies for the development of an active ML-based pH adjustment strategy of multi-buffered poly-protic mixtures. By employing TL, benefiting from previously generated data, we aim to demonstrate this novel strategy for pH adjustment and keep the process efficient, even under an extreme low data regime.