We have developed a self-driving pH robot to autonomously perform small scale batch titration of viscous liquid formulations, which is a challenging, yet common industrial task. This titration is often carried out using a weak, polyprotic titrant, namely, citric acid as it confers certain benefits to the final formulation. This necessitated the advancement of a machinelearning driven titration algorithm to incorporate a physical chemistry model for the weak titrant's pH-dependent speciation state. The final presented pH adjustment strategy is a multistage algorithm capable of adjusting a wide variety of systems using any combination of strongstrong or weak-strong acid/base pairs. The method is able to recover and successfully titrate even those samples which have overshot the set point and therefore resulted in the formation of a potentially complex buffer system in solution. Across a range of over 250 samples, it typically took two to five iterations to converge on the pH target. The titration algorithm is run onboard the pH robot, resulting in the platform's self-driving capability, and has additionally been shared as a standalone user-friendly web application. This web application was utilised to successfully drive the pH adjustment of scaled up formulation samples, closer to industrial R&D volumes, within an average of just three iterations. Finally, a power-law relationship was experimentally defined between the optimal mixing time after acid/base addition and the formulation's viscosity. Overall, a "physics-informed" ML method and titration robot have been presented to accelerate industrially-relevant pH adjustment workflows.