High　optical　transmittance　(T%)　has　always　been　an　important　indicator　of　transparent-ferroelectric　ceramics　for　optoelectronic　coupling.　However,　the　pathway　of　pursuing　high　transparency　has　been　at　the　experimental　trial-and-error　stage　over　the　past　decades,　manifesting　major　drawbacks　of　being　time-consuming　and　resource-wasting.　The　present　work　introduces　a　machine　learning　(ML)　accelerated　development　of　highly　transparent-ferroelectrics　by　taking　potassium-sodium　niobate　(KNN)-based　ceramics　as　the　model　material.　It　is　highlighted　that　by　using　a　small　data　set　of　118　sample　data　and　four　key　features,　we　predict　the　T%　of　un-synthesized　KNN-based　ceramics　and　evaluate　the　importance　of　key　features.　Meanwhile,　the　screened　(K0.5Na0.5)(0.956)Tb0.004Ba0.04NbO3　ceramics　were　successfully　realized　by　the　conventional　solid-state　synthesis,　and　the　experimental　measured　T%　is　in　full　agreement　with　the　predicted　results,　exhibiting　a　satisfactory　high　T%　of　similar　to　78%　at　800　nm.　In　addition,　ML　is　also　used　to　explore　the　best　experimental　parameters,　and　the　prediction　results　of　T%　are　particularly　sensitive　to　changes　in　sintering　temperature　(ST).　Eventually,　the　predicted　optimal　ST　is　highly　consistent　with　the　experimental　one.　This　study　constructs　a　new　avenue　for　exploring　high　T%　ferroelectric　KNN　ceramics　based　on　ML,　ascertaining　optimal　process　parameters,　and　guiding　the　development　of　other　transparent-ferroelectrics　in　optoelectronic　fields.