diff --git a/_plotly_utils/basevalidators.py b/_plotly_utils/basevalidators.py
index 0d7e387bbc3..c4d40f9e178 100644
--- a/_plotly_utils/basevalidators.py
+++ b/_plotly_utils/basevalidators.py
@@ -22,6 +22,20 @@ def fullmatch(regex, string, flags=0):
     return re.match("(?:" + regex_string + r")\Z", string, flags=flags)
 
 
+def to_non_numpy_type(np, v):
+    """
+    Convert a numpy scalar value to a native Python type.
+    Calling .item() on a datetime64[ns] value returns an integer, since
+    Python datetimes only support microsecond precision. So we cast
+    datetime64[ns] to datetime64[us] to ensure it remains a datetime.
+
+    Should only be used in contexts where we already know `np` is defined.
+    """
+    if hasattr(v, "dtype") and v.dtype == np.dtype("datetime64[ns]"):
+        return v.astype("datetime64[us]").item()
+    return v.item()
+
+
 # Utility functions
 # -----------------
 def to_scalar_or_list(v):
@@ -35,12 +49,12 @@ def to_scalar_or_list(v):
     np = get_module("numpy", should_load=False)
     pd = get_module("pandas", should_load=False)
     if np and np.isscalar(v) and hasattr(v, "item"):
-        return v.item()
+        return to_non_numpy_type(np, v)
     if isinstance(v, (list, tuple)):
         return [to_scalar_or_list(e) for e in v]
     elif np and isinstance(v, np.ndarray):
         if v.ndim == 0:
-            return v.item()
+            return to_non_numpy_type(np, v)
         return [to_scalar_or_list(e) for e in v]
     elif pd and isinstance(v, (pd.Series, pd.Index)):
         return [to_scalar_or_list(e) for e in v]
diff --git a/tests/test_optional/test_graph_objs/test_numpy.py b/tests/test_optional/test_graph_objs/test_numpy.py
new file mode 100644
index 00000000000..a234dc47830
--- /dev/null
+++ b/tests/test_optional/test_graph_objs/test_numpy.py
@@ -0,0 +1,16 @@
+from datetime import datetime
+
+import numpy as np
+
+import plotly.graph_objs as go
+
+
+def test_np_ns_datetime():
+    x = [np.datetime64("2025-09-26").astype("datetime64[ns]")]
+    y = [1.23]
+    scatter = go.Scatter(x=x, y=y, mode="markers")
+
+    # x value should be converted to native datetime
+    assert isinstance(scatter.x[0], datetime)
+    # x value should match original numpy value at microsecond precision
+    assert x[0].astype("datetime64[us]").item() == scatter.x[0]