diff --git a/data_structures/disjoint_set/progressive_set_intersection.py b/data_structures/disjoint_set/progressive_set_intersection.py
new file mode 100644
index 000000000000..eb202d77c99d
--- /dev/null
+++ b/data_structures/disjoint_set/progressive_set_intersection.py
@@ -0,0 +1,47 @@
+"""Progressive multi-set intersection optimized for imbalanced sets."""
+
+from typing import Set
+
+
+def progressive_set_intersection(*sets: Set) -> Set:
+    """
+    Compute the intersection of multiple sets efficiently.
+
+    This function sorts the input sets by size (smallest first) and
+    progressively intersects them. It includes early termination when
+    the result becomes empty, which is very effective when dealing with
+    many sets or highly imbalanced sizes (e.g., one small set + many large ones).
+
+    Python's built-in `set.intersection(*sets)` is already optimized in C,
+    but this implementation demonstrates the "smallest-first + prune early"
+    heuristic for educational purposes.
+
+    Time Complexity: Better than naive in practice due to early pruning.
+
+    Examples:
+        >>> progressive_set_intersection({1, 2, 3}, {2, 3, 4}, {2, 5})
+        {2}
+        >>> progressive_set_intersection({1, 2}, {3, 4})
+        set()
+        >>> progressive_set_intersection({10, 20, 30})
+        {10, 20, 30}
+        >>> progressive_set_intersection()
+        set()
+    """
+    if not sets:
+        return set()
+
+    if len(sets) == 1:
+        return sets[0].copy()
+
+    # Sort by length (smallest first) for optimal pruning
+    sorted_sets = sorted(sets, key=len)
+
+    result = sorted_sets[0].copy()
+
+    for current_set in sorted_sets[1:]:
+        if not result:
+            return set()
+        result &= current_set   # Efficient in-place intersection
+
+    return result