Anaerobic　co-metabolism　is　a　biotechnological　process　that　improves　biodegradation　efficiency　of　refractory　organics.　By　adding　cosubstrates,　it　provides　additional　carbon　sources　and　energy　for　the　microbial　metabolic　degradation　of　organic　matter.　However,　a　large　number　of　repeated　experiments　are　required　to　screen　out　suitable　conditions.　It　is　typically　lengthy　and　carries　significant　uncertainty.　In　this　study,　machine　learning　(ML)　was　used　to　drive　the　screening　of　anaerobic　degradation　conditions　for　ceftriaxone　sodium　(CTX)　in　wastewater　treatment.　The　results　showed　that　the　XGBoost　algorithm　was　able　to　effectively　predict　the　decomposition　efficiency　with　an　accuracy　of　up　to　95%.　A　Shapley　additive　explanation　(SHAP)　analysis　showed　that　temperature,　pH,　and　CTX/glucose　ratio　had　the　greatest　impacts　on　the　removal　efficiency　of　CTX,　thus　highlighting　the　remarkable　ability　of　ML　to　accelerate　the　screening　of　optimal　degradation　conditions.　A　high-throughput　analysis　proved　that　the　dominant　genera　and　microbial　structures　presented　under　the　two　environmental　conditions　with　the　largest　significant　difference　(temperature,　CTX/glucose　ratio)　were　significantly　different　and　that　bacterial　genera　such　as　Fastidiosipila,　norank_f_Prolixibacteraceae,　norank_f_Bacteroidetes_vadinHA17,　and　Georgenia　significantly　affected　the　hydrolysis　and　acidification　process.　This　work　stands　out　by　integrating　advanced　ML　techniques　into　environmental　engineering,　thereby　enhancing　efficiency　and　providing　richer　analytical　insights　compared　to　traditional　methods.　©　2024　American　Chemical　Society.