diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 8048b62029a0..57a1cf1e5d11 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -811,7 +811,8 @@ def histogram(a, bins=10, range=None, density=None, weights=None):
         n = np.zeros(n_equal_bins, ntype)
 
         # Pre-compute histogram scaling factor
-        norm = n_equal_bins / _unsigned_subtract(last_edge, first_edge)
+        norm_numerator = n_equal_bins
+        norm_denom = _unsigned_subtract(last_edge, first_edge)
 
         # We iterate over blocks here for two reasons: the first is that for
         # large arrays, it is actually faster (for example for a 10^8 array it
@@ -839,7 +840,8 @@ def histogram(a, bins=10, range=None, density=None, weights=None):
 
             # Compute the bin indices, and for values that lie exactly on
             # last_edge we need to subtract one
-            f_indices = _unsigned_subtract(tmp_a, first_edge) * norm
+            f_indices = ((_unsigned_subtract(tmp_a, first_edge) / norm_denom)
+                         * norm_numerator)
             indices = f_indices.astype(np.intp)
             indices[indices == n_equal_bins] -= 1
 
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py
index 87e6e1d41c4a..38b3d3dcbf3f 100644
--- a/numpy/lib/tests/test_histograms.py
+++ b/numpy/lib/tests/test_histograms.py
@@ -408,6 +408,13 @@ def test_big_arrays(self):
         hist = np.histogramdd(sample=sample, bins=(xbins, ybins, zbins))
         assert_equal(type(hist), type((1, 2)))
 
+    def test_gh_23110(self):
+        hist, e = np.histogram(np.array([-0.9e-308], dtype='>f8'),
+                               bins=2,
+                               range=(-1e-308, -2e-313))
+        expected_hist = np.array([1, 0])
+        assert_array_equal(hist, expected_hist)
+
 
 class TestHistogramOptimBinNums:
     """