As　the　most　representative　and　widely　utilized　hole　transport　material　(HTM),　spiro-OMeTAD　encounters　challenges　including　limited　hole　mobility,　high　production　costs,　and　demanding　synthesis　conditions.　These　issues　have　a　notable　impact　on　the　overall　performance　of　perovskite　solar　cells　(PSCs)　based　on　spiro-OMeTAD　and　hinder　its　large-scale　commercial　application.　Consequently,　there　exists　a　strong　demand　for　high-throughput　computational　design　of　novel　small-molecule　HTMs　(SM-HTMs)　that　are　cost-effective,　easy　to　synthesize,　and　offer　excellent　performance.　In　this　study,　a　systematic　and　iterative　design　and　development　process　for　SM　HTMs　is　proposed,　aiming　to　accelerate　the　discovery　and　application　of　high-performance　SM-HTMs.　A　custom-developed　molecular　splicing　algorithm　(MSA)　generated　a　sample　space　of　200,000　intermediate　molecules,　culminating　in　the　creation　of　a　comprehensive　database　of　over　7,000　potential　SM-HTM　candidates.　In　total,　six　promising　HTM　candidates　were　identified　through　MSA,　density　functional　theory　calculations　and　high-throughput　screening.　Furthermore,　three　machine　learning　algorithms,　namely　random　forest,　gradient　boosting　decision　tree,　and　extreme　gradient　boosting　(XGBoost),　were　employed　to　construct　predictive　models　for　key　material　properties,　including　hole　reorganization　energy,　solvation　free　energy,　maximum　absorption　wavelength,　and　hydrophobicity.　Among　these,　the　XGBoost-based　model　demonstrated　the　best　overall　performance.　The　MSA　methodology　combining　comprehensive　SM-HTM　database　and　performance　prediction　models,　as　introduced　in　this　study,　offers　a　powerful　and　universal　toolkit　for　the　design　and　optimization　of　next-generation　SM-HTMs,　thereby　paving　the　way　for　future　advancements　of　PSCs.