diff --git a/doc/whats_new/v1.2.rst b/doc/whats_new/v1.2.rst
index e3c5d46bc8e2a..5f45cdf6a7168 100644
--- a/doc/whats_new/v1.2.rst
+++ b/doc/whats_new/v1.2.rst
@@ -41,6 +41,9 @@ Changelog
   :pr:`20802` by :user:`Brandon Pokorny <Clickedbigfoot>`,
   and :pr:`22965` by :user:`Meekail Zain <micky774>`.
 
+- |Enhancement| :class:`cluster.Birch` now preserves dtype for `numpy.float32`
+  inputs. :pr:`22968` by `Meekail Zain <micky774>`.
+
 Code and Documentation Contributors
 -----------------------------------
 
diff --git a/sklearn/cluster/_birch.py b/sklearn/cluster/_birch.py
index cfdfeab27b15c..2bfdd2971e4d4 100644
--- a/sklearn/cluster/_birch.py
+++ b/sklearn/cluster/_birch.py
@@ -60,12 +60,14 @@ def _split_node(node, threshold, branching_factor):
         branching_factor=branching_factor,
         is_leaf=node.is_leaf,
         n_features=node.n_features,
+        dtype=node.init_centroids_.dtype,
     )
     new_node2 = _CFNode(
         threshold=threshold,
         branching_factor=branching_factor,
         is_leaf=node.is_leaf,
         n_features=node.n_features,
+        dtype=node.init_centroids_.dtype,
     )
     new_subcluster1.child_ = new_node1
     new_subcluster2.child_ = new_node2
@@ -147,7 +149,7 @@ class _CFNode:
 
     """
 
-    def __init__(self, *, threshold, branching_factor, is_leaf, n_features):
+    def __init__(self, *, threshold, branching_factor, is_leaf, n_features, dtype):
         self.threshold = threshold
         self.branching_factor = branching_factor
         self.is_leaf = is_leaf
@@ -156,8 +158,8 @@ def __init__(self, *, threshold, branching_factor, is_leaf, n_features):
         # The list of subclusters, centroids and squared norms
         # to manipulate throughout.
         self.subclusters_ = []
-        self.init_centroids_ = np.zeros((branching_factor + 1, n_features))
-        self.init_sq_norm_ = np.zeros((branching_factor + 1))
+        self.init_centroids_ = np.zeros((branching_factor + 1, n_features), dtype=dtype)
+        self.init_sq_norm_ = np.zeros((branching_factor + 1), dtype)
         self.squared_norm_ = []
         self.prev_leaf_ = None
         self.next_leaf_ = None
@@ -221,7 +223,9 @@ def insert_cf_subcluster(self, subcluster):
             # subcluster to accommodate the new child.
             else:
                 new_subcluster1, new_subcluster2 = _split_node(
-                    closest_subcluster.child_, threshold, branching_factor
+                    closest_subcluster.child_,
+                    threshold,
+                    branching_factor,
                 )
                 self.update_split_subclusters(
                     closest_subcluster, new_subcluster1, new_subcluster2
@@ -552,7 +556,11 @@ def _fit(self, X, partial):
         first_call = not (partial and has_root)
 
         X = self._validate_data(
-            X, accept_sparse="csr", copy=self.copy, reset=first_call
+            X,
+            accept_sparse="csr",
+            copy=self.copy,
+            reset=first_call,
+            dtype=[np.float64, np.float32],
         )
         threshold = self.threshold
         branching_factor = self.branching_factor
@@ -568,6 +576,7 @@ def _fit(self, X, partial):
                 branching_factor=branching_factor,
                 is_leaf=True,
                 n_features=n_features,
+                dtype=X.dtype,
             )
 
             # To enable getting back subclusters.
@@ -576,6 +585,7 @@ def _fit(self, X, partial):
                 branching_factor=branching_factor,
                 is_leaf=True,
                 n_features=n_features,
+                dtype=X.dtype,
             )
             self.dummy_leaf_.next_leaf_ = self.root_
             self.root_.prev_leaf_ = self.dummy_leaf_
@@ -600,6 +610,7 @@ def _fit(self, X, partial):
                     branching_factor=branching_factor,
                     is_leaf=False,
                     n_features=n_features,
+                    dtype=X.dtype,
                 )
                 self.root_.append_subcluster(new_subcluster1)
                 self.root_.append_subcluster(new_subcluster2)
@@ -714,7 +725,7 @@ def transform(self, X):
             Transformed data.
         """
         check_is_fitted(self)
-        self._validate_data(X, accept_sparse="csr", reset=False)
+        X = self._validate_data(X, accept_sparse="csr", reset=False)
         with config_context(assume_finite=True):
             return euclidean_distances(X, self.subcluster_centers_)
 
@@ -758,3 +769,6 @@ def _global_clustering(self, X=None):
 
         if compute_labels:
             self.labels_ = self._predict(X)
+
+    def _more_tags(self):
+        return {"preserves_dtype": [np.float64, np.float32]}
diff --git a/sklearn/cluster/tests/test_birch.py b/sklearn/cluster/tests/test_birch.py
index e0051704653ae..c5d88c2bc6f0e 100644
--- a/sklearn/cluster/tests/test_birch.py
+++ b/sklearn/cluster/tests/test_birch.py
@@ -17,6 +17,7 @@
 from sklearn.utils._testing import assert_almost_equal
 from sklearn.utils._testing import assert_array_equal
 from sklearn.utils._testing import assert_array_almost_equal
+from sklearn.utils._testing import assert_allclose
 
 
 def test_n_samples_leaves_roots():
@@ -228,3 +229,20 @@ def test_feature_names_out():
 
     names_out = brc.get_feature_names_out()
     assert_array_equal([f"birch{i}" for i in range(n_clusters)], names_out)
+
+
+def test_transform_match_across_dtypes():
+    X, _ = make_blobs(n_samples=80, n_features=4, random_state=0)
+    brc = Birch(n_clusters=4)
+    Y_64 = brc.fit_transform(X)
+    Y_32 = brc.fit_transform(X.astype(np.float32))
+
+    assert_allclose(Y_64, Y_32, atol=1e-6)
+
+
+def test_subcluster_dtype(global_dtype):
+    X = make_blobs(n_samples=80, n_features=4, random_state=0)[0].astype(
+        global_dtype, copy=False
+    )
+    brc = Birch(n_clusters=4)
+    assert brc.fit(X).subcluster_centers_.dtype == global_dtype