Context　Acromegaly,　caused　by　excess　GH　and　IGF-1　due　to　pituitary　adenomas,　often　necessitates　first-generation　somatostatin　receptor　ligands　(fgSRLs)　therapy　when　surgery　fails.　However,　responses　to　fgSRLs　therapy　vary　widely.Objective　To　develop　a　machine　learning　(ML)-based　calculator　that　predicts　individual　responses　to　fgSRLs　therapy,　enabling　evidence-based　acromegaly　management.Design　A　retrospective　study　(January　2010-July　2024)　utilizing　the　Research　Patient　Data　Registry　to　evaluate　10　ML　algorithms　and　create　a　predictive　calculator.Setting　Single-center　study　conducted　at　Mass　General　Brigham-affiliated　hospitals.Patients　One　hundred　eleven　acromegaly　patients　met　inclusion　criteria,　classified　as　fgSRLs-responsive　(n　=　64)　or　fgSRLs-resistant　(n　=　47).Interventions　IGF-1　trajectories　were　analyzed　using　linear　mixed-effects　modeling.　Ten　ML　algorithms　were　assessed　to　predict　fgSRLs　resistance.　SHapley　Additive　exPlanations　(SHAP)　analysis　identified　key　predictors　for　the　development　of　a　web-based　clinical　calculator.Main　Outcome　Measures　Model　performance　was　primarily　evaluated　using　area　under　the　receiver　operating　characteristic　curve　(AUROC),　along　with　accuracy,　precision,　recall,　specificity,　F1　score,　and　decision　curve　analysis　(DCA).Results　The　CatBoost　model　exhibited　optimal　performance　based　on　AUROC　0.896　(95%　confidence　interval:　0.751-0.990),　with　accuracy　82.4%,　precision　86.7%,　specificity　88.2%,　and　F1　score　81.2%.　Key　predictors　of　fgSRLs　resistance　identified　via　SHAP　analysis　included　pre-fgSRLs　treatment　GH,　Knosp　grade,　pre-fgSRLs　treatment　IGF-1　index,　T2-weighted　magnetic　resonance　imaging　density,　and　comorbidity　burden.　The　model　demonstrated　excellent　calibration　(Brier　score　0.131)　and　clinical　utility　via　DCA.　A　web-based　calculator　was　developed　for　clinical　use.Conclusion　The　CatBoost-based　calculator　effectively　predicts　fgSRLs　treatment　response　in　acromegaly　patients.　Prospective　validation　is　required　before　clinical　implementation.