Hybrid　organic-inorganic　perovskites　(HOIPs)　solar　cells　have　presented　broad　application　prospects　in　the　photovoltaic　field　due　to　their　high　energy　conversion　efficiency,　ease　of　preparation,　and　low　production　costs.　With　the　flourishing　development　of　artificial　intelligence,　machine　learning　(ML)　has　been　recently　used　for　novel　HOIP　designs.　However,　the　practical　application　of　ML　models　for　the　designing　of　HOIPs　is　hampered　mainly　due　to　the　lack　of　interpretability.　Herein,　a　data-driven　interpretable　ML　approach　is　introduced　to　distill　the　universal　simple　descriptors　for　the　power　conversion　efficiency　(PCE)　of　HOIPs-based　solar　cells.　It　is　highlighted　that　two　descriptors　consist　of　easily　obtained　parameters　are　proposed　to　accurately　predict　PCE,　which　are　superior　to　the　commonly　used　descriptor　band　gap　(Eg).　Remarkably,　universal　criterions　for　the　highthroughput　screening　of　HOIPs　are　proposed　to　accelerate　the　screening　of　HOIPs-based　solar　cells　with　high　PCE　performance.　This　work　paves　the　way　toward　rapid　and　precise　screening　of　efficient　HOIPs-based　solar　cells　using　a　data-driven　interpretable　ML　approach.