Predicting activation energies for reaction steps is essential for modeling catalytic processes, but accurate barrier simulations often require considerable computational expense, especially for electrochemical reactions. Given the challenges of barrier computations and the growing promise of electrochemical routes for various processes, generalizable energetic trends in electrochemistry can significantly aid in analyzing reaction networks and building microkinetic models. Herein, we employ density functional theory and machine learning nudged elastic band models to simulate electrochemical protonation of *C, *N, and *O monatomic adsorbates from hydronium on a series of transition metal surfaces. We observe a consistent trend of decreasing protonation reaction energies yet increasing activation barriers from *O to *N to *C. Analysis of bond orders and reaction pathways provides insight into the origin of the observed trends in protonation energetics. We hypothesize that these results are relevant for polyatomic adsorbates, which can simplify analysis of reaction mechanisms and inform catalyst design.