diff --git a/sklearn/cluster/tests/test_birch.py b/sklearn/cluster/tests/test_birch.py
index 99f9b1cdbc9fe..c2f3c06d15ba7 100644
--- a/sklearn/cluster/tests/test_birch.py
+++ b/sklearn/cluster/tests/test_birch.py
@@ -17,9 +17,10 @@
 from sklearn.utils._testing import assert_allclose
 
 
-def test_n_samples_leaves_roots(global_random_seed):
+def test_n_samples_leaves_roots(global_random_seed, global_dtype):
     # Sanity check for the number of samples in leaves and roots
     X, y = make_blobs(n_samples=10, random_state=global_random_seed)
+    X = X.astype(global_dtype, copy=False)
     brc = Birch()
     brc.fit(X)
     n_samples_root = sum([sc.n_samples_ for sc in brc.root_.subclusters_])
@@ -30,9 +31,10 @@ def test_n_samples_leaves_roots(global_random_seed):
     assert n_samples_root == X.shape[0]
 
 
-def test_partial_fit(global_random_seed):
+def test_partial_fit(global_random_seed, global_dtype):
     # Test that fit is equivalent to calling partial_fit multiple times
     X, y = make_blobs(n_samples=100, random_state=global_random_seed)
+    X = X.astype(global_dtype, copy=False)
     brc = Birch(n_clusters=3)
     brc.fit(X)
     brc_partial = Birch(n_clusters=None)
@@ -47,10 +49,11 @@ def test_partial_fit(global_random_seed):
     assert_array_equal(brc_partial.subcluster_labels_, brc.subcluster_labels_)
 
 
-def test_birch_predict(global_random_seed):
+def test_birch_predict(global_random_seed, global_dtype):
     # Test the predict method predicts the nearest centroid.
     rng = np.random.RandomState(global_random_seed)
     X = generate_clustered_data(n_clusters=3, n_features=3, n_samples_per_cluster=10)
+    X = X.astype(global_dtype, copy=False)
 
     # n_samples * n_samples_per_cluster
     shuffle_indices = np.arange(30)
@@ -58,6 +61,10 @@ def test_birch_predict(global_random_seed):
     X_shuffle = X[shuffle_indices, :]
     brc = Birch(n_clusters=4, threshold=1.0)
     brc.fit(X_shuffle)
+
+    # Birch must preserve inputs' dtype
+    assert brc.subcluster_centers_.dtype == global_dtype
+
     assert_array_equal(brc.labels_, brc.predict(X_shuffle))
     centroids = brc.subcluster_centers_
     nearest_centroid = brc.subcluster_labels_[
@@ -66,9 +73,10 @@ def test_birch_predict(global_random_seed):
     assert_allclose(v_measure_score(nearest_centroid, brc.labels_), 1.0)
 
 
-def test_n_clusters(global_random_seed):
+def test_n_clusters(global_random_seed, global_dtype):
     # Test that n_clusters param works properly
     X, y = make_blobs(n_samples=100, centers=10, random_state=global_random_seed)
+    X = X.astype(global_dtype, copy=False)
     brc1 = Birch(n_clusters=10)
     brc1.fit(X)
     assert len(brc1.subcluster_centers_) > 10
@@ -88,9 +96,10 @@ def test_n_clusters(global_random_seed):
         brc4.fit(X)
 
 
-def test_sparse_X(global_random_seed):
+def test_sparse_X(global_random_seed, global_dtype):
     # Test that sparse and dense data give same results
     X, y = make_blobs(n_samples=100, centers=10, random_state=global_random_seed)
+    X = X.astype(global_dtype, copy=False)
     brc = Birch(n_clusters=10)
     brc.fit(X)
 
@@ -98,6 +107,9 @@ def test_sparse_X(global_random_seed):
     brc_sparse = Birch(n_clusters=10)
     brc_sparse.fit(csr)
 
+    # Birch must preserve inputs' dtype
+    assert brc_sparse.subcluster_centers_.dtype == global_dtype
+
     assert_array_equal(brc.labels_, brc_sparse.labels_)
     assert_allclose(brc.subcluster_centers_, brc_sparse.subcluster_centers_)
 
@@ -122,9 +134,10 @@ def check_branching_factor(node, branching_factor):
             check_branching_factor(cluster.child_, branching_factor)
 
 
-def test_branching_factor(global_random_seed):
+def test_branching_factor(global_random_seed, global_dtype):
     # Test that nodes have at max branching_factor number of subclusters
     X, y = make_blobs(random_state=global_random_seed)
+    X = X.astype(global_dtype, copy=False)
     branching_factor = 9
 
     # Purposefully set a low threshold to maximize the subclusters.
@@ -146,9 +159,10 @@ def check_threshold(birch_instance, threshold):
         current_leaf = current_leaf.next_leaf_
 
 
-def test_threshold(global_random_seed):
+def test_threshold(global_random_seed, global_dtype):
     # Test that the leaf subclusters have a threshold lesser than radius
     X, y = make_blobs(n_samples=80, centers=4, random_state=global_random_seed)
+    X = X.astype(global_dtype, copy=False)
     brc = Birch(threshold=0.5, n_clusters=None)
     brc.fit(X)
     check_threshold(brc, 0.5)