Electrochemical oxidation (EO) has come into focus for the degradation of pharmaceuticals (PhCs). However, optimizing EO to simultaneously degrade diverse pharmaceutical structures remains challenging due to limited kinetic data and the complex interplay between molecular properties and reaction conditions. Accurate prediction of apparent first-order degradation rate constants (k) would be beneficial for estimating degradation efficiency and optimizing the treatment process. In this work, two graph-based deep learning models and ten traditional machine learning (ML) algorithms were first developed to predict 355 log₂k values derived from 31 unique pharmaceutical compounds. While XGBoost achieved the highest predictive accuracy, the graph-based models uniquely enabled interpretable mechanistic insights, identifying critical functional groups (phenyl, primary amine, carbonyl, secondary amine) and their reactivity patterns. Feature importance analysis revealed that, besides the evident parameters anode area and current density, electrolyte type (e.g., NaCl) and molecular topological complexity were the most influential parameters for EO performance. Coupling with density functional theory (CDFT) and Fukui function analysis further highlighted electrophilic and nucleophilic attack sites within the molecules. Overall, this study integrates traditional ML algorithms and graph-based models to investigate the factors governing EO degradation kinetics of pharmaceuticals and to provide mechanistic insights into structure–reactivity relationships. The developed framework is particularly useful for interpreting the influence of operational conditions and molecular structures on degradation kinetics, although its transferability to entirely unseen pharmaceuticals remains limited by the current availability and diversity of published degradation data.