Electrochemistry has emerged as an increasingly viable tool in molecular synthesis. Here the authors realize electrocatalyzed C-H activations, with the aid of data science and artificial intelligence, towards selective alkenylations for late-stage drug diversifications. Electrooxidation has emerged as an increasingly viable platform in molecular syntheses that can avoid stoichiometric chemical redox agents. Despite major progress in electrochemical C-H activations, these arene functionalizations generally require directing groups to enable the C-H activation. The installation and removal of these directing groups call for additional synthesis steps, which jeopardizes the inherent efficacy of the electrochemical C-H activation approach, leading to undesired waste with reduced step and atom economy. In sharp contrast, herein we present palladium-electrochemical C-H olefinations of simple arenes devoid of exogenous directing groups. The robust electrocatalysis protocol proved amenable to a wide range of both electron-rich and electron-deficient arenes under exceedingly mild reaction conditions, avoiding chemical oxidants. This study points to an interesting approach of two electrochemical transformations for the success of outstanding levels of position-selectivities in direct olefinations of electron-rich anisoles. A physical organic parameter-based machine learning model was developed to predict position-selectivity in electrochemical C-H olefinations. Furthermore, late-stage functionalizations set the stage for the direct C-H olefinations of structurally complex pharmaceutically relevant compounds, thereby avoiding protection and directing group manipulations.