Enhanced　coagulation　with　pre-oxidation　is　a　cost-effective　approach　for　managing　seasonal　water　quality　pollution　caused　by　algal　outbreaks　and　deaths.　The　synergistic　application　of　pre-oxidants　and　coagulants,　coupled　with　intelligent　and　precise　dosage　control,　constitutes　a　prominent　research　focus　in　water　treatment　field.　This　study　evaluates　the　removal　of　various　pre-oxidants,　including　potassium　permanganate　(KMnO4),　KMnO4　composites　(PPC),　and　potassium　ferrate　(K2FeO4),　in　combination　with　coagulants　like　polyaluminum　chloride　(PACl)　and　aluminum　sulfate　(Al-2(SO4)(3)).　A　machine　learning　algorithm,　based　on　the　least　squares　support　vector　machine　(LSSVM),　was　developed　to　predict　optimal　dosages.　After　15　months　of　source　water　quality　monitoring,　algal　contaminations　predominantly　driven　by　cyanobacteria-dominated　became　worse　particularly　in　the　high　temperature　and　algae　period　and　autumn,　which　were　positively　correlated　with　UV254　and　CODMn　(p　＜　0.05).　The　doses　of　PACl　and　Al-2(SO4)(3)　were　between　100　mu　M　and　120　mu　M　(calculated　as　Al)　across　various　periods　to　efficiently　remove　organic　matter.　Under　optimal　chemical　dosages,　pre-oxidation　facilitated　the　protein-like　substances　removal.　The　removal　efficiency　of　PPC　surpassed　that　of　KMnO4　and　K2FeO4.　The　LSSVM　model　demonstrated　superior　predictive　performance　for　dosages　compared　to　other　models　like　random　forest　(RF)　and　back　propagation　(BP)　neural　networks,　with　feature　importance　analysis　identifying　water　temperature,　UV254,　and　conductivity　as　the　core　parameters　for　real-time　dosing　systems.　This　study　elucidated　dosing　strategies　alongside　algae　contaminations　removal　associated　with　pre-oxidation　enhanced　coagulation　while　proposing　a　methodology　for　dynamically　adjusting　oxidant　and　coagulant　dosages　through　real-time　monitoring　of　both　raw　water　quality　and　effluent　from　coagulation-precipitation　processes,　thereby　providing　novel　insights　into　precise　real-time　dosing　for　chemicals　in　water　treatment　facilities.