diff --git a/CHANGELOG.md b/CHANGELOG.md
index 752240a4734..a7fa2f1ee74 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -67,6 +67,7 @@ This release is compatible with NumPy 2.5.
 * Fixed `dpnp.tensor.acosh` and `dpnp.tensor.acos` returning infinity for complex numbers with large negative real parts [#2928](https://github.com/IntelPython/dpnp/pull/2928)
 * Fixed `__array_namespace_info__().devices()` and `.default_device()` to return Python array API compatible device objects [#2979](https://github.com/IntelPython/dpnp/pull/2979)
 * Fixed `dpnp.interp` with an empty input array `x` to return an empty array with the correct dtype [#2985](https://github.com/IntelPython/dpnp/pull/2985)
+* Fixed `dpnp.interp` returning `nan` when querying at an exact knot point whose adjacent `fp` value is `inf` [#2986](https://github.com/IntelPython/dpnp/pull/2986)
 
 ### Security
 
diff --git a/dpnp/backend/kernels/elementwise_functions/interpolate.hpp b/dpnp/backend/kernels/elementwise_functions/interpolate.hpp
index c85dafea24b..704820ad3c1 100644
--- a/dpnp/backend/kernels/elementwise_functions/interpolate.hpp
+++ b/dpnp/backend/kernels/elementwise_functions/interpolate.hpp
@@ -88,6 +88,9 @@ class InterpolateFunctor
         else if (x_idx >= static_cast<TIdx>(xp_size - 1)) {
             out[id] = right_val;
         }
+        else if (x_val == xp[x_idx]) {
+            out[id] = fp[x_idx];
+        }
         else {
             TValue slope =
                 (fp[x_idx + 1] - fp[x_idx]) / (xp[x_idx + 1] - xp[x_idx]);
diff --git a/dpnp/tests/third_party/cupy/math_tests/test_misc.py b/dpnp/tests/third_party/cupy/math_tests/test_misc.py
index 930930d18f4..f478e0fbf4d 100644
--- a/dpnp/tests/third_party/cupy/math_tests/test_misc.py
+++ b/dpnp/tests/third_party/cupy/math_tests/test_misc.py
@@ -626,6 +626,18 @@ def test_interp_inf_to_nan(self, xp, dtype_y, dtype_x):
         fy = xp.asarray([0, 10], dtype=dtype_y)
         return xp.interp(x, fx, fy)
 
+    @testing.for_float_dtypes(name="dtype_x")
+    @testing.for_dtypes("efdFD", name="dtype_y")
+    @testing.numpy_cupy_allclose(atol=1e-5, type_check=has_support_aspect64())
+    def test_interp_inf_fy_at_knot(self, xp, dtype_y, dtype_x):
+        # Regression test: querying at an exact knot point should return the
+        # knot value, not nan, even when the adjacent fy value is inf.
+        # See https://github.com/cupy/cupy/issues/9823
+        x = xp.asarray([2.0], dtype=dtype_x)
+        fx = xp.asarray([1.0, 2.0, 3.0, 4.0], dtype=dtype_x)
+        fy = xp.asarray([1.0, 2.0, xp.inf, 4.0], dtype=dtype_y)
+        return xp.interp(x, fx, fy)
+
     @testing.for_all_dtypes(name="dtype_2", no_bool=True, no_complex=True)
     @testing.for_all_dtypes(name="dtype_1", no_bool=True, no_complex=True)
     @testing.numpy_cupy_array_equal(type_check=has_support_aspect64())