One of the greatest challenges in process development is the limited amount of data that can be collected. Techniques that can draw insights from this limited data have the possibility to accelerate process development. This thesis presents a collection of studies on using transfer learning to accelerate various aspects of process development. Part I focuses on reaction optimization, where I propose a benchmarking framework for comparing machine learning strategies for reaction optimization and demonstrate the benefits of using multi-task learning to accelerate chemical reaction optimization. In Part II, I explore the use of reinforcement learning and multi-fidelity Bayesian optimization for accelerating feedback controller tuning, specifically for distillation control systems. Finally, in Part III, I take two perspectives on using machine learning for predictive thermodynamics, a key aspect of process modelling. I introduce DeepGamma for predicting activity coefficients and ML-SAFT for predicting PCP-SAFT parameters, showing steps towards improving thermodynamic predictions using transfer learning. Together, all of these studies demonstrate the potential of transfer learning to accelerate process development, providing valuable insights for future research and practical applications.