This work demonstrates an active learning approach to develop a transferable machine learning-based potential (MLP) that can be used to study surface diffusion phenomena at ab initio accuracy. Specifically, we focus on modelling the surface dynamics of 8 commonly studied adsorbates on a prototypical Ag(111) surface. The resulting diffusivities, obtained by fitting mean square deviations from molecular dynamics simulations, highlight potential shortcomings of traditional NEB-based estimates. The active learning approach allows the model to identify configurations that require further refinement; this strategy is particularly advantageous for describing the dynamics of complex adsorbates. Furthermore, the model demonstrates good transferability and can be fine-tuned to study unseen adsorbates by including small amounts of additional DFT data. Thus, this work illustrates a valuable MLP-based workflow for studying surface diffusion that can be easily generalized to other