In　summer,　Ozone　(O3)　pollution　and　urban　heat　island　(UHI)　pose　serious　health　risks　to　humans.　To　obtain　the　spatial　distributions　of　ozone　and　urban　heat　island　in　Xi＇an　in　summer　and　develop　a　simultaneous　control　strategy　of　ozone　and　urban　heat　island,　the　land　use　regression　model　is　modified　and　improved　using　the　machine　learning　random　forest　algorithm.　The　LUR-Kriging-RF　integrated　prediction　model　is　then　established.　The　land　use　regression　and　kriging　are　used　to　extract　the　feature　variables,　while　random　forest　is　used　to　establish　a　regression　model.　The　spatial　distribution　maps　of　ozone　and　urban　heat　island　in　Xi＇an　are　obtained　by　regression　mapping　of　the　prediction　model,　and　the　spatial　relationships　between　them　are　analyzed.　The　SHapley　Additive　explanation　(SHAP)　and　partial　dependence　plot　(PDP)　are　adopted　to　explain　the　way　feature　variables　act　on　ozone　and　urban　heat　island.　Based　on　the　spatial　distribution　and　interaction　mode,　a　simultaneous　control　strategy　of　ozone　and　urban　heat　island　in　Xi＇an　is　put　forward.　For　ozone,　the　R2　of　the　integrated　prediction　model　(0.65)　is　higher　than　that　of　land　use　regression　(0.4),　while　the　RMSE　(28.18)　of　the　integrated　model　is　lower　than　that　of　land　use　regression　(35.66).　For　temperature,　the　R2　of　the　integrated　model　(0.93)　is　higher　than　that　of　land　use　regression　(0.8),　while　its　RMSE　(0.92)　is　lower　than　that　of　land　use　regression　(1.52).　The　performance　of　the　LUR-Kriging-RF　integrated　prediction　model　is　better　than　that　of　land　use　regression.　This　study　reveals　the　spatial　interactions　between　ozone　and　land　use　regression　in　the　central　urban　areas.　The　suitable　strategies　for　mapping　ozone　pollution　and　urban　heat　island　control　include　reducing　VOCs　emissions　from　industrial　sources　and　agricultural　sources,　increasing　plants　with　low　VOCs　emissions,　and　spray　humidification.　This　study　can　be　used　to　evaluate　ozone　exposure　and　thermal　exposure,　provide　scientific　support　for　environmental　protection　and　urban　heat　island　control　policies,　contribute　to　reducing　public　health　threats,　promote　the　sustainability　of　urban　environments,　and　promote　the　practical　application　of　machine　learning　in　this　field.　©　2021　Elsevier　Ltd