diff --git a/pyiceberg/table/upsert_util.py b/pyiceberg/table/upsert_util.py
index 723a89aa20..c12351d45c 100644
--- a/pyiceberg/table/upsert_util.py
+++ b/pyiceberg/table/upsert_util.py
@@ -16,12 +16,14 @@
 # under the License.
 import functools
 import operator
+from typing import List, cast
 
 import pyarrow as pa
 from pyarrow import Table as pyarrow_table
 from pyarrow import compute as pc
 
 from pyiceberg.expressions import (
+    AlwaysFalse,
     And,
     BooleanExpression,
     EqualTo,
@@ -36,7 +38,16 @@ def create_match_filter(df: pyarrow_table, join_cols: list[str]) -> BooleanExpre
     if len(join_cols) == 1:
         return In(join_cols[0], unique_keys[0].to_pylist())
     else:
-        return Or(*[And(*[EqualTo(col, row[col]) for col in join_cols]) for row in unique_keys.to_pylist()])
+        filters: List[BooleanExpression] = [
+            cast(BooleanExpression, And(*[EqualTo(col, row[col]) for col in join_cols])) for row in unique_keys.to_pylist()
+        ]
+
+        if len(filters) == 0:
+            return AlwaysFalse()
+        elif len(filters) == 1:
+            return filters[0]
+        else:
+            return functools.reduce(lambda a, b: Or(a, b), filters)
 
 
 def has_duplicate_rows(df: pyarrow_table, join_cols: list[str]) -> bool:
@@ -86,7 +97,7 @@ def get_rows_to_update(source_table: pa.Table, target_table: pa.Table, join_cols
     if rows_to_update:
         rows_to_update_table = pa.concat_tables(rows_to_update)
     else:
-        rows_to_update_table = pa.Table.from_arrays([], names=source_table.column_names)
+        rows_to_update_table = source_table.schema.empty_table()
 
     common_columns = set(source_table.column_names).intersection(set(target_table.column_names))
     rows_to_update_table = rows_to_update_table.select(list(common_columns))
diff --git a/tests/table/test_upsert.py b/tests/table/test_upsert.py
index 0cfb0ba609..c97015e650 100644
--- a/tests/table/test_upsert.py
+++ b/tests/table/test_upsert.py
@@ -23,8 +23,11 @@
 
 from pyiceberg.catalog import Catalog
 from pyiceberg.exceptions import NoSuchTableError
+from pyiceberg.expressions import And, EqualTo, Reference
+from pyiceberg.expressions.literals import LongLiteral
 from pyiceberg.schema import Schema
 from pyiceberg.table import UpsertResult
+from pyiceberg.table.upsert_util import create_match_filter
 from pyiceberg.types import IntegerType, NestedField, StringType
 from tests.catalog.test_base import InMemoryCatalog, Table
 
@@ -366,3 +369,22 @@ def test_upsert_with_identifier_fields(catalog: Catalog) -> None:
 
     assert upd.rows_updated == 1
     assert upd.rows_inserted == 1
+
+
+def test_create_match_filter_single_condition() -> None:
+    """
+    Test create_match_filter with a composite key where the source yields exactly one unique key.
+    Expected: The function returns the single And condition directly.
+    """
+
+    data = [
+        {"order_id": 101, "order_line_id": 1, "extra": "x"},
+        {"order_id": 101, "order_line_id": 1, "extra": "x"},  # duplicate
+    ]
+    schema = pa.schema([pa.field("order_id", pa.int32()), pa.field("order_line_id", pa.int32()), pa.field("extra", pa.string())])
+    table = pa.Table.from_pylist(data, schema=schema)
+    expr = create_match_filter(table, ["order_id", "order_line_id"])
+    assert expr == And(
+        EqualTo(term=Reference(name="order_id"), literal=LongLiteral(101)),
+        EqualTo(term=Reference(name="order_line_id"), literal=LongLiteral(1)),
+    )