diff --git a/doc/whats_new/v1.0.rst b/doc/whats_new/v1.0.rst
index 154c32617c4ba..78dc95026c45e 100644
--- a/doc/whats_new/v1.0.rst
+++ b/doc/whats_new/v1.0.rst
@@ -70,6 +70,9 @@ Changelog
   in multicore settings. :pr:`19052` by
   :user:`Yusuke Nagasaka <YusukeNagasaka>`.
 
+- |Fix| Fixes incorrect multiple data-conversion warnings when clustering
+  boolean data. :pr:`19046` by :user:`Surya Prakash <jdsurya>`.
+
 :mod:`sklearn.linear_model`
 ...........................
 
diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py
index 8998963704562..11893dbd70520 100755
--- a/sklearn/cluster/_optics.py
+++ b/sklearn/cluster/_optics.py
@@ -14,6 +14,8 @@
 import warnings
 import numpy as np
 
+from ..exceptions import DataConversionWarning
+from ..metrics.pairwise import PAIRWISE_BOOLEAN_FUNCTIONS
 from ..utils import gen_batches, get_chunk_n_rows
 from ..utils.validation import _deprecate_positional_args
 from ..neighbors import NearestNeighbors
@@ -243,7 +245,15 @@ def fit(self, X, y=None):
         self : instance of OPTICS
             The instance.
         """
-        X = self._validate_data(X, dtype=float)
+
+        dtype = bool if self.metric in PAIRWISE_BOOLEAN_FUNCTIONS else float
+        if dtype == bool and X.dtype != bool:
+            msg = (f"Data will be converted to boolean for"
+                   f" metric {self.metric}, to avoid this warning,"
+                   f" you may convert the data prior to calling fit.")
+            warnings.warn(msg, DataConversionWarning)
+
+        X = self._validate_data(X, dtype=dtype)
 
         if self.cluster_method not in ['dbscan', 'xi']:
             raise ValueError("cluster_method should be one of"
diff --git a/sklearn/cluster/tests/test_optics.py b/sklearn/cluster/tests/test_optics.py
index 4428b6c00d7eb..d5b30256d4943 100644
--- a/sklearn/cluster/tests/test_optics.py
+++ b/sklearn/cluster/tests/test_optics.py
@@ -10,6 +10,7 @@
 from sklearn.datasets import make_blobs
 from sklearn.cluster import OPTICS
 from sklearn.cluster._optics import _extend_region, _extract_xi_labels
+from sklearn.exceptions import DataConversionWarning
 from sklearn.metrics.cluster import contingency_matrix
 from sklearn.metrics.pairwise import pairwise_distances
 from sklearn.cluster import DBSCAN
@@ -213,6 +214,49 @@ def test_bad_reachability():
         clust.fit(X)
 
 
+def test_nowarn_if_metric_bool_data_bool():
+    # make sure no warning is raised if metric and data are both boolean
+    # non-regression test for
+    # https://github.com/scikit-learn/scikit-learn/issues/18996
+
+    pairwise_metric = 'rogerstanimoto'
+    X = np.random.randint(2, size=(5, 2), dtype=np.bool)
+
+    with pytest.warns(None) as warn_record:
+        OPTICS(metric=pairwise_metric).fit(X)
+        assert len(warn_record) == 0
+
+
+def test_warn_if_metric_bool_data_no_bool():
+    # make sure a *single* conversion warning is raised if metric is boolean
+    # but data isn't
+    # non-regression test for
+    # https://github.com/scikit-learn/scikit-learn/issues/18996
+
+    pairwise_metric = 'rogerstanimoto'
+    X = np.random.randint(2, size=(5, 2), dtype=np.int)
+    msg = f"Data will be converted to boolean for metric {pairwise_metric}"
+
+    with pytest.warns(DataConversionWarning, match=msg) as warn_record:
+        OPTICS(metric=pairwise_metric).fit(X)
+        assert len(warn_record) == 1
+
+
+def test_nowarn_if_metric_no_bool():
+    # make sure no conversion warning is raised if
+    # metric isn't boolean, no matter what the data type is
+    pairwise_metric = 'minkowski'
+    X_bool = np.random.randint(2, size=(5, 2), dtype=np.bool)
+    X_num = np.random.randint(2, size=(5, 2), dtype=np.int)
+
+    with pytest.warns(None) as warn_record:
+        # fit boolean data
+        OPTICS(metric=pairwise_metric).fit(X_bool)
+        # fit numeric data
+        OPTICS(metric=pairwise_metric).fit(X_num)
+        assert len(warn_record) == 0
+
+
 def test_close_extract():
     # Test extract where extraction eps is close to scaled max_eps