Our study demonstrates the efficacy of our optimisation framework in an automated, high-throughput setting. Initial HTE plate selection with quasi-random sampling ensures comprehensive coverage of large reaction spaces to identify promising local optima and reactivity crevices. Subsequent model-driven Bayesian optimisation iteratively refines reaction objectives, while purely exploitative optimisation in the final round leverages all accumulated knowledge to maximise final reaction outcomes. We successfully validated our ML-driven optimisation workflow on a challenging Nicatalysed Suzuki reaction in a 96-well HTE campaign, demonstrating advantages over traditional, purely experimentalist-guided methods. By integrating machine learning strategies with highly parallel automation, our workflow demonstrates real-world applicability for accelerating reaction optimisation and is currently being implemented in several process chemistry projects at Roche. New values of parameters such as ligands and solvents could also be added to the search space as optimisation proceeds , and the workflow can be applied to other chemical reactions of interest, including those outside of HTE settings.