Predicting　the　natural　distribution　of　heavy　metals　(HMs)　in　soil　is　important　to　understand　the　potential　risk　of　pollution.　However,　suitable　technologies　are　still　lacking　for　wide　scale　due　to　the　large　spatial　heterogeneity.　In　this　study,　we　developed　machine　learning　models　for　predicting　natural　contents　of　five　typical　HMs　in　soil,　including　As,　Cd,　Cr,　Hg　and　Pb.　It　was　found　that　the　optional　random　forest　(RF)　model　had　the　best　performance　with　the　R-2　up　to　0.64.　Based　on　this　model,　potential　distribution　of　the　five　HMs　explored　that　elevated　contents　were　mainly　concentrated　in　the　southwest　and　south　central　of　China.　Feature　analysis　illustrated　that　importance　of　natural　factors　followed　the　order　of　geological　attributes　＞　soil　properties　＞　climatic　conditions　＞　ecological　functions.　In　particular,　lithology　of　the　parent　material　dominated　the　content　of　metals,　with　the　contributions　of　18-25%.　Moreover,　soil　properties　of　pH,　cation　exchange　capacity,　profile　depth　of　soil　and　vegetation　coverage　had　different　influences　on　HMs,　due　to　the　variability　in　the　properties　of　different　HMs.　This　study　developed　a　mapping　relationship　between　natural　factors　and　soil　HMs　by　data　science　method,　which　may　provide　instructive　information　for　pollution　control　and　planning　decisions.