Knowing　the　correlation　of　reaction　parameters　in　the　preparation　process　of　carbon　dots　(CDs)　is　essential　for　optimizing　the　synthesis　strategy,　exploring　exotic　properties,　and　exploiting　potential　applications.　However,　the　integrated　screening　experimental　data　on　the　synthesis　of　CDs　are　huge　and　noisy.　Machine　learning　(ML)　has　recently　been　successfully　used　for　the　screening　of　high-performance　materials.　Here,　we　demonstrate　how　ML-based　techniques　can　offer　insight　into　the　successful　prediction,　optimization,　and　acceleration　of　CDs＇　synthesis　process.　A　regression　ML　model　on　hydrothermal-synthesized　CDs　is　established　capable　of　revealing　the　relationship　between　various　synthesis　parameters　and　experimental　outcomes　as　well　as　enhancing　the　process-related　properties　such　as　the　fluorescent　quantum　yield　(QY).　CDs　exhibiting　a　strong　green　emission　with　QY　up　to　39.3%　are　obtained　through　the　combined　ML　guidance　and　experimental　verification.　The　mass　of　precursors　and　the　volume　of　alkaline　catalysts　are　identified　as　the　most　important　features　in　the　synthesis　of　high-QY　CDs　by　the　trained　ML　model.　The　CDs　are　applied　as　an　ultrasensitive　fluorescence　probe　for　monitoring　the　Fe3+　ion　because　of　their　superior　optical　behaviors.　The　probe　exhibits　the　linear　response　to　the　Fe3+　ion　with　a　wide　concentration　range　(0-150　mu　M),　and　its　detection　limit　is　0.039　mu　M.　Our　findings　demonstrate　the　great　capability　of　ML　to　guide　the　synthesis　of　high-quality　CDs,　accelerating　the　development　of　intelligent　material.