The　conventional　theories　to　predict　the　oxygen　evolution　reaction　(OER)　performance　in　electrochemical　water-electrolysis,　including　the　d-band　center　and　the　e(g)　orbital　occupancy,　encounter　limitations　under　specific　conditions.　The　d-band　center　serves　as　a　partial　descriptor　of　adsorption　energy,　leading　to　inconsistencies,　and　the　e(g)　orbital　occupancy　theory　underestimates　the　contributions　of　other　orbitals.　Here,　a　machine　learning-assisted　molecular　orbital　investigation　is　conducted　to　explore　3d　orbitals　characteristics.　To　account　for　the　crystal　field　effect　and　mitigate　partition　errors　arising　from　orbital　degeneracy,　3d　orbitals　are　categorized　into　e(g)　and　t(2g).　The　proposed　descriptors　are　designed　not　only　to　predict　performance　but　also　to　aid　in　elucidating　the　underlying　determinants　of　performance.　It　elucidates　nuanced　performance　determinants　that　are　context-dependent　and　can　be　categorized　into　two　distinct　types:　electron-deficient,　e.g.,　Fe　(3d(6))　and　Co　(3d(7)),　and　electron-rich,　e.g.,　Cu　(3d(9))　and　Zn　(3d(10)).　For　electron-deficient　metals,　the　orbitals　are　unoccupied,　with　the　electrons　populating　the　t(2g)　orbital　preferentially　released　as　the　valence　state　increases,　thereby　influencing　performance,　and　vice　versa.　In　summary,　this　work　establishes　a　complex　correlation　between　molecular　orbitals　and　catalytic　activity　via　ML,　offering　a　novel　perspective　for　advancing　the　design　and　elucidating　the　mechanisms　of　high-performance　OER　electrocatalysts.