Iron (Fe) oxidation is a fundamental phenomenon that is scientifically vital and technologically significant. Despite substantial experimental efforts, accurate atomistic modelling of Fe oxidation remains elusive, which requires a reliable interatomic potential, whose development hinges on a representative first-principles training dataset. However, Fe-oxygen (O) system is known for its structural complexity, rendering the generation of high-quality first-principles dataset challenging. In this work, we propose the Perturbation Augmented Space group structure Sampling (PASS) method to generate extensive dataset for Fe-O system consisting of small-cell structures (no more than 10 atoms). Based on atomic cluster expansion (ACE) framework, we present a systematic approach to developing a transferable machine learning interatomic potential (MLIP). The accuracy and capability of the ACE potential are thoroughly validated across pure Fe and Fe-O systems through bulk, surface, and interface properties. The large-scale Fe oxidation simulations with the ACE potential capture key features of the oxidation process, including the formation of an FeO-like structure consistent with recent experimental observations. This work demonstrates that the PASS method yields an accurate and transferable MLIP that can capture the reactive complexity of oxide growth while remaining computationally practical for extended systems.