diff --git a/astropy/modeling/tests/test_constraints.py b/astropy/modeling/tests/test_constraints.py
index 2ddcf37d607..1e7ac2a3bb8 100644
--- a/astropy/modeling/tests/test_constraints.py
+++ b/astropy/modeling/tests/test_constraints.py
@@ -14,13 +14,9 @@
 from astropy.modeling import fitting, models
 from astropy.modeling.core import Fittable1DModel
 from astropy.modeling.parameters import Parameter
-from astropy.utils import minversion
-from astropy.utils.compat.numpycompat import NUMPY_LT_2_0
 from astropy.utils.compat.optional_deps import HAS_SCIPY
 from astropy.utils.exceptions import AstropyUserWarning
 
-SCIPY_LT_1_11_2 = not minversion("scipy", "1.11.2") if HAS_SCIPY else True
-
 fitters = [
     fitting.LevMarLSQFitter,
     fitting.TRFLSQFitter,
@@ -184,6 +180,7 @@ def test_bounds_slsqp(self):
         assert intercept + 10**-5 >= bounds["intercept"][0]
         assert intercept - 10**-5 <= bounds["intercept"][1]
 
+    @pytest.mark.filterwarnings("ignore:The fit may be unsuccessful")
     @pytest.mark.parametrize("fitter", fitters)
     def test_bounds_gauss2d_lsq(self, fitter):
         fitter = fitter()
@@ -205,19 +202,13 @@ def test_bounds_gauss2d_lsq(self, fitter):
             bounds=bounds,
         )
         if isinstance(fitter, (fitting.LevMarLSQFitter, fitting.DogBoxLSQFitter)):
-            with pytest.warns(AstropyUserWarning, match="The fit may be unsuccessful"):
-                model = fitter(gauss, X, Y, self.data)
+            model = fitter(gauss, X, Y, self.data)
         else:
-            ctx2 = nullcontext()
             if isinstance(fitter, fitting.TRFLSQFitter):
                 ctx = np.errstate(invalid="ignore", divide="ignore")
-                if not NUMPY_LT_2_0 or not SCIPY_LT_1_11_2:
-                    ctx2 = pytest.warns(
-                        AstropyUserWarning, match="The fit may be unsuccessful"
-                    )
             else:
                 ctx = nullcontext()
-            with ctx, ctx2:
+            with ctx:
                 model = fitter(gauss, X, Y, self.data)
         x_mean = model.x_mean.value
         y_mean = model.y_mean.value