The　escalating　introduction　of　pesticides/veterinary　drugs　into　the　environment　has　necessitated　a　rapid　evaluation　of　their　potential　risks　to　ecosystems　and　human　health.　The　developmental　toxicity　of　pesticides/veterinary　drugs　was　less　explored,　and　much　less　the　large-scale　predictions　for　untested　pesticides,　veterinary　drugs　and　bio-pesticides.　Alternative　methods　like　quantitative　structure-activity　relationship　(QSAR)　are　promising　because　their　potential　to　ensure　the　sustainable　and　safe　use　of　these　chemicals.　We　collected　133　pesticides　and　veterinary　drugs　with　half-maximal　active　concentration　(AC(50))　as　the　zebrafish　embryo　developmental　toxicity　endpoint.　The　QSAR　model　development　adhered　to　rigorous　OECD　principles,　ensuring　that　the　model　possessed　good　internal　robustness　(R-2　＞　0.6　and　Q(LOO)(2)　＞　0.6)　and　external　predictivity　(R-2　test　＞　0.7,　Q(Fn)(2)　＞0.7,　and　CCCtest　＞　0.85).　To　further　enhance　the　predictive　performance　of　the　model,　a　quantitative　readacross　structure-activity　relationship　(q-RASAR)　model　was　established　using　the　combined　set　of　RASAR　and　2D　descriptors.　Mechanistic　interpretation　revealed　that　dipole　moment,　the　presence　of　C-O　fragment　at　10　topological　distance,　molecular　size,　lipophilicity,　and　Euclidean　distance　(ED)-based　RA　function　were　main　factors　influencing　toxicity.　For　the　first　time,　the　established　QSAR　and　q-RASAR　models　were　combined　to　prioritize　the　developmental　toxicity　of　a　vast　array　of　true　external　compounds　(pesticides/veterinary　drugs/bio-pesticides)　lacking　experimental　values.　The　prediction　reliability　of　each　query　molecule　was　evaluated　by　leverage　approach　and　prediction　reliability　indicator.　Overall,　the　dual　computational　toxicology　models　can　inform　decision-making　and　guide　the　design　of　new　pesticides/veterinary　drugs　with　improved　safety　profiles.