diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst
index 00c957448550f..d1cfe0d42239c 100644
--- a/doc/whats_new/v0.22.rst
+++ b/doc/whats_new/v0.22.rst
@@ -817,6 +817,10 @@ Changelog
   :class:`preprocessing.KernelCenterer`
   :pr:`14336` by :user:`Gregory Dexter <gdex1>`.
 
+- |Fix| :class:`preprocessing.QuantileTransformer` now guarantees the 
+  `quantiles_` attribute to be completely sorted in non-decreasing manner.
+  :pr:`15751` by :user:`Tirth Patel <tirthasheshpatel>`.
+
 :mod:`sklearn.model_selection`
 ..............................
 
diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index 9227efa958b43..b047908842b38 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -2262,6 +2262,11 @@ def _dense_fit(self, X, random_state):
                 col = col.take(subsample_idx, mode='clip')
             self.quantiles_.append(np.nanpercentile(col, references))
         self.quantiles_ = np.transpose(self.quantiles_)
+        # Due to floating-point precision error in `np.nanpercentile`,
+        # make sure that quantiles are monotonically increasing.
+        # Upstream issue in numpy:
+        # https://github.com/numpy/numpy/issues/14685
+        self.quantiles_ = np.maximum.accumulate(self.quantiles_)
 
     def _sparse_fit(self, X, random_state):
         """Compute percentiles for sparse matrices.
@@ -2305,6 +2310,11 @@ def _sparse_fit(self, X, random_state):
                 self.quantiles_.append(
                         np.nanpercentile(column_data, references))
         self.quantiles_ = np.transpose(self.quantiles_)
+        # due to floating-point precision error in `np.nanpercentile`,
+        # make sure the quantiles are monotonically increasing
+        # Upstream issue in numpy:
+        # https://github.com/numpy/numpy/issues/14685
+        self.quantiles_ = np.maximum.accumulate(self.quantiles_)
 
     def fit(self, X, y=None):
         """Compute the quantiles used for transforming.
diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
index 9a8e31d468f1c..cdff446cb336c 100644
--- a/sklearn/preprocessing/tests/test_data.py
+++ b/sklearn/preprocessing/tests/test_data.py
@@ -25,6 +25,7 @@
 from sklearn.utils._testing import assert_allclose
 from sklearn.utils._testing import assert_allclose_dense_sparse
 from sklearn.utils._testing import skip_if_32bit
+from sklearn.utils._testing import _convert_container
 
 from sklearn.utils.sparsefuncs import mean_variance_axis
 from sklearn.preprocessing._data import _handle_zeros_in_scale
@@ -1532,6 +1533,26 @@ def test_quantile_transform_nan():
     assert not np.isnan(transformer.quantiles_[:, 1:]).any()
 
 
+@pytest.mark.parametrize("array_type", ['array', 'sparse'])
+def test_quantile_transformer_sorted_quantiles(array_type):
+    # Non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/15733
+    # Taken from upstream bug report:
+    # https://github.com/numpy/numpy/issues/14685
+    X = np.array([0, 1, 1, 2, 2, 3, 3, 4, 5, 5, 1, 1, 9, 9, 9, 8, 8, 7] * 10)
+    X = 0.1 * X.reshape(-1, 1)
+    X = _convert_container(X, array_type)
+
+    n_quantiles = 100
+    qt = QuantileTransformer(n_quantiles=n_quantiles).fit(X)
+
+    # Check that the estimated quantile threasholds are monotically
+    # increasing:
+    quantiles = qt.quantiles_[:, 0]
+    assert len(quantiles) == 100
+    assert all(np.diff(quantiles) >= 0)
+
+
 def test_robust_scaler_invalid_range():
     for range_ in [
         (-1, 90),