Industries are increasingly reliant on advanced process modeling techniques to improve development and operational efficiency. Utilising these models for optimisation holds the potential to significantly enhance performance, reduce costs, and ensure the highest standards of quality. However, when the underlying models become too complex or computationally expensive, surrogate-based optimisation offers a viable solution. In this work, we introduce a multi-target tree regression approach designed to address the complexities of multi-objective optimisation. The proposed methodology simultaneously handles multiple outputs, effectively captures nonlinear relationships, and enhances interpretability, making it a powerful tool for process optimisation. Additionally, we propose a novel methodology to mitigate the challenges of high dimensionality which is inherent in large datasets, enabling more efficient use of mathematical programming surrogates. By leveraging the developed methodologies, we aim to implement multi-objective optimisation to optimise key performance metrics like yield and purity in a real-world Active Pharmaceutical Ingredient Manufacturing Case Study, while deriving a Pareto curve to effectively illustrate the trade-off between competing objectives.