The　formation　of　oil–mineral　aggregates　(OMAs)　is　essential　for　understanding　the　behavior　of　oil　spills　in　estuaries　and　coastal　waters.　We　utilized　statistical　methods　(screening　design)　to　identify　the　most　influential　variables　(seven　factors　in　total)　during　OMA　formation.　Time　was　the　most　important　factor,　followed　by　temperature　and　oil/clay　ratio.　Moreover,　machine　learning　was　applied　to　predict　the　OMA　median　diameter　(D50).　Among　the　three　tested　algorithms,　the　Random　Forest　(RF)　algorithm　showed　the　highest　accuracy,　with　a　training　R2　of　0.99　and　testing　R2　of　0.93.　An　open-source　software　tool　that　integrates　the　RF　algorithm　was　developed,　allowing　users　to　easily　estimate　the　OMA　D50　based　on　input　variables.　The　valuable　results　and　the　practical　tool　we　have　developed　enhance　the　understanding　and　management　of　environmental　impacts　associated　with　oil　spills.