diff --git a/graphs/swim_in_rising_water.py b/graphs/swim_in_rising_water.py
new file mode 100644
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+++ b/graphs/swim_in_rising_water.py
@@ -0,0 +1,74 @@
+import heapq
+
+
+def swim_in_rising_water(grid: list[list[int]]) -> int:
+    """
+    Return the minimum time to reach the bottom right square of the grid
+    from the top left, where time t allows swimming to cells with
+    elevation <= t.
+
+    This is a variant of Dijkstra's shortest path algorithm using a
+    priority queue (min-heap) to find the minimum elevation (time) path
+    in a grid graph.
+
+    Reference: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm
+
+    :param grid: n x n integer matrix where grid[i][j] is the elevation
+                 at (i, j)
+    :return: Minimum time to reach (n-1, n-1) from (0, 0)
+
+    Examples:
+    >>> swim_in_rising_water([[0, 2], [1, 3]])
+    3
+    >>> grid = [
+    ...     [0, 1, 2, 3, 4],
+    ...     [24, 23, 22, 21, 5],
+    ...     [12, 13, 14, 15, 16],
+    ...     [11, 17, 18, 19, 20],
+    ...     [10, 9, 8, 7, 6]
+    ... ]
+    >>> swim_in_rising_water(grid)
+    16
+    >>> swim_in_rising_water([[0]])  # n=1 edge case
+    0
+    >>> swim_in_rising_water([[5, 3], [4, 2]])  # Another small grid
+    5
+    """
+    if not grid or not grid[0]:
+        raise ValueError("Grid must be a non-empty n x n matrix")
+
+    n = len(grid)
+    if n != len(grid[0]):
+        raise ValueError("Grid must be square (n x n)")
+
+    # Directions: right, down, left, up
+    directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]
+
+    # Min-heap: (max_elevation_so_far, row, col)
+    min_heap: list[tuple[int, int, int]] = [(grid[0][0], 0, 0)]
+    visited = [[False] * n for _ in range(n)]
+    visited[0][0] = True
+
+    while min_heap:
+        max_elev, r, c = heapq.heappop(min_heap)
+
+        # Reached bottom-right
+        if r == n - 1 and c == n - 1:
+            return max_elev
+
+        for dr, dc in directions:
+            nr, nc = r + dr, c + dc
+            if 0 <= nr < n and 0 <= nc < n and not visited[nr][nc]:
+                visited[nr][nc] = True
+                # The time is the max elevation encountered on this path
+                new_elev = max(max_elev, grid[nr][nc])
+                heapq.heappush(min_heap, (new_elev, nr, nc))
+
+    # Should always reach if grid is valid, but for completeness
+    raise ValueError("No path found to bottom-right (grid constraints violated)")
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()