diff --git a/searches/A_star_search.py b/searches/A_star_search.py
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+# Python program for A* Search Algorithm
+import math
+import heapq
+from typing import List, Tuple
+
+# Define the Cell class
+
+
+class Cell:
+    def __init__(self) -> None:
+        # Parent cell's row index
+        self.parent_i = 0
+        # Parent cell's column index
+        self.parent_j = 0
+        # Total cost of the cell (g + h)
+        self.f = float("inf")
+        # Cost from start to this cell
+        self.g = float("inf")
+        # Heuristic cost from this cell to destination
+        self.h = 0
+
+
+# Define the size of the grid
+ROW = 9
+COL = 10
+
+# Check if a cell is valid (within the grid)
+
+
+def is_valid(row: int, col: int) -> bool:
+    """
+    Check if a cell is within the grid bounds.
+
+    Args:
+        row (int): The row index of the cell.
+        col (int): The column index of the cell.
+
+    Returns:
+        bool: True if the cell is within bounds, False otherwise.
+
+    Examples:
+        >>> is_valid(5, 3)
+        True
+        >>> is_valid(9, 5)
+        False
+        >>> is_valid(-1, 0)
+        False
+    """
+    return (row >= 0) and (row < ROW) and (col >= 0) and (col < COL)
+
+
+# Check if a cell is unblocked
+
+
+def is_unblocked(grid: List[List[int]], row: int, col: int) -> bool:
+    """
+    Check if a cell is unblocked in the grid.
+
+    Args:
+        grid (List[List[int]]): The grid representing the map.
+        row (int): The row index of the cell.
+        col (int): The column index of the cell.
+
+    Returns:
+        bool: True if the cell is unblocked (1), False if blocked (0).
+
+    Examples:
+        >>> grid = [
+        ...     [1, 0, 1],
+        ...     [1, 1, 0],
+        ...     [0, 1, 1]
+        ... ]
+        >>> is_unblocked(grid, 0, 0)
+        True
+        >>> is_unblocked(grid, 0, 1)
+        False
+        >>> is_unblocked(grid, 2, 2)
+        True
+    """
+    return grid[row][col] == 1
+
+
+# Check if a cell is the destination
+
+
+def is_destination(row: int, col: int, dest: Tuple[int, int]) -> bool:
+    """
+    Check if a cell is the destination.
+
+    Args:
+        row (int): The row index of the cell.
+        col (int): The column index of the cell.
+        dest (Tuple[int, int]): The destination coordinates as (row, col).
+
+    Returns:
+        bool: True if the cell is the destination, False otherwise.
+
+    Examples:
+        >>> is_destination(3, 4, (3, 4))
+        True
+        >>> is_destination(2, 1, (3, 4))
+        False
+    """
+    return row == dest[0] and col == dest[1]
+
+
+# Calculate the heuristic value of a cell (Euclidean distance to destination)
+
+
+def calculate_h_value(row: int, col: int, dest: Tuple[int, int]) -> float:
+    """
+    Calculate the heuristic value (Euclidean distance) from a cell to the destination.
+
+    Args:
+        row (int): The row index of the cell.
+        col (int): The column index of the cell.
+        dest (Tuple[int, int]): The destination coordinates as (row, col).
+
+    Returns:
+        float: The Euclidean distance from the current cell to the destination.
+
+    Examples:
+        >>> calculate_h_value(0, 0, (3, 4))
+        5.0
+        >>> calculate_h_value(2, 1, (2, 1))
+        0.0
+        >>> calculate_h_value(1, 1, (4, 5))
+        5.0
+    """
+    return ((row - dest[0]) ** 2 + (col - dest[1]) ** 2) ** 0.5
+
+
+# Trace the path from source to destination
+
+
+def trace_path(cell_details: List[List[Cell]], dest: Tuple[int, int]) -> None:
+    """
+    Trace and print the path from the source to the destination.
+
+    Args:
+        cell_details (List[List[Cell]]): A 2D list containing details of each cell.
+        dest (Tuple[int, int]): The destination coordinates as (row, col).
+
+    Returns:
+        None
+
+    Examples:
+        >>> class Cell:
+        ...     def __init__(self):
+        ...         self.parent_i = 0
+        ...         self.parent_j = 0
+        >>> cell_details = [[Cell() for _ in range(2)] for _ in range(2)]
+        >>> cell_details[1][1].parent_i = 0
+        >>> cell_details[1][1].parent_j = 0
+        >>> trace_path(cell_details, (1, 1))
+        The Path is
+        -> (0, 0) -> (1, 1)
+    """
+    print("The Path is ")
+    path = []
+    row = dest[0]
+    col = dest[1]
+
+    # Trace the path from destination to source using parent cells
+    while not (
+        cell_details[row][col].parent_i == row
+        and cell_details[row][col].parent_j == col
+    ):
+        path.append((row, col))
+        temp_row = cell_details[row][col].parent_i
+        temp_col = cell_details[row][col].parent_j
+        row = temp_row
+        col = temp_col
+
+    # Add the source cell to the path
+    path.append((row, col))
+    # Reverse the path to get the path from source to destination
+    path.reverse()
+
+    # Print the path
+    for i in path:
+        print("->", i, end=" ")
+    print()
+
+
+# Implement the A* search algorithm
+
+
+def a_star_search(
+    grid: List[List[int]], src: Tuple[int, int], dest: Tuple[int, int]
+) -> None:
+    """
+    Perform the A* search to find the shortest path from source to destination.
+
+    Args:
+        grid (List[List[int]]): The grid representing the map, where 1 is unblocked and 0 is blocked.
+        src (Tuple[int, int]): The source coordinates as (row, col).
+        dest (Tuple[int, int]): The destination coordinates as (row, col).
+
+    Returns:
+        None
+
+    Examples:
+        >>> grid = [
+        ...     [1, 1, 1],
+        ...     [1, 0, 1],
+        ...     [1, 1, 1]
+        ... ]
+        >>> a_star_search(grid, (0, 0), (2, 2))
+        The destination cell is found
+        The Path is
+        -> (0, 0) -> (1, 1) -> (2, 2)
+        >>> a_star_search(grid, (0, 0), (1, 1))
+        Source or the destination is blocked
+    """
+    # Check if the source and destination are valid
+    if not is_valid(src[0], src[1]) or not is_valid(dest[0], dest[1]):
+        print("Source or destination is invalid")
+        return
+
+    # Check if the source and destination are unblocked
+    if not is_unblocked(grid, src[0], src[1]) or not is_unblocked(
+        grid, dest[0], dest[1]
+    ):
+        print("Source or the destination is blocked")
+        return
+
+    # Check if we are already at the destination
+    if is_destination(src[0], src[1], dest):
+        print("We are already at the destination")
+        return
+
+    # Initialize the closed list (visited cells)
+    closed_list = [[False for _ in range(COL)] for _ in range(ROW)]
+    # Initialize the details of each cell
+    cell_details = [[Cell() for _ in range(COL)] for _ in range(ROW)]
+
+    # Initialize the start cell details
+    i = src[0]
+    j = src[1]
+    cell_details[i][j].f = 0
+    cell_details[i][j].g = 0
+    cell_details[i][j].h = 0
+    cell_details[i][j].parent_i = i
+    cell_details[i][j].parent_j = j
+
+    # Initialize the open list (cells to be visited) with the start cell
+    open_list = []
+    heapq.heappush(open_list, (0.0, i, j))
+
+    # Initialize the flag for whether destination is found
+    found_dest = False
+
+    # Main loop of A* search algorithm
+    while len(open_list) > 0:
+        # Pop the cell with the smallest f value from the open list
+        p = heapq.heappop(open_list)
+
+        # Mark the cell as visited
+        i = p[1]
+        j = p[2]
+        closed_list[i][j] = True
+
+        # For each direction, check the successors
+        directions = [
+            (0, 1),
+            (0, -1),
+            (1, 0),
+            (-1, 0),
+            (1, 1),
+            (1, -1),
+            (-1, 1),
+            (-1, -1),
+        ]
+        for dir in directions:
+            new_i = i + dir[0]
+            new_j = j + dir[1]
+
+            # If the successor is valid, unblocked, and not visited
+            if (
+                is_valid(new_i, new_j)
+                and is_unblocked(grid, new_i, new_j)
+                and not closed_list[new_i][new_j]
+            ):
+                # If the successor is the destination
+                if is_destination(new_i, new_j, dest):
+                    # Set the parent of the destination cell
+                    cell_details[new_i][new_j].parent_i = i
+                    cell_details[new_i][new_j].parent_j = j
+                    print("The destination cell is found")
+                    # Trace and print the path from source to destination
+                    trace_path(cell_details, dest)
+                    found_dest = True
+                    return
+                else:
+                    # Calculate the new f, g, and h values
+                    g_new = cell_details[i][j].g + 1.0
+                    h_new = calculate_h_value(new_i, new_j, dest)
+                    f_new = g_new + h_new
+
+                    # If the cell is not in the open list or the new f value is smaller
+                    if (
+                        cell_details[new_i][new_j].f == float("inf")
+                        or cell_details[new_i][new_j].f > f_new
+                    ):
+                        # Add the cell to the open list
+                        heapq.heappush(open_list, (f_new, new_i, new_j))
+                        # Update the cell details
+                        cell_details[new_i][new_j].f = f_new
+                        cell_details[new_i][new_j].g = g_new
+                        cell_details[new_i][new_j].h = h_new
+                        cell_details[new_i][new_j].parent_i = i
+                        cell_details[new_i][new_j].parent_j = j
+
+    # If the destination is not found after visiting all cells
+    if not found_dest:
+        print("Failed to find the destination cell")
+
+
+# Driver Code
+
+if __name__ == "__main__":
+    """
+    Run the A* search algorithm on a predefined grid.
+
+    Returns:
+        None
+
+    Examples:
+        >>> main()
+        The destination cell is found
+        The Path is
+        -> (8, 0) -> (7, 1) -> (6, 0) -> (5, 1) -> (4, 0) -> (3, 1) -> (2, 0) -> (1, 1) -> (0, 0)
+    """
+    # Define the grid (1 for unblocked, 0 for blocked)
+    grid = [
+        [1, 0, 1, 1, 1, 1, 0, 1, 1, 1],
+        [1, 1, 1, 0, 1, 1, 1, 0, 1, 1],
+        [1, 1, 1, 0, 1, 1, 0, 1, 0, 1],
+        [0, 0, 1, 0, 1, 0, 0, 0, 0, 1],
+        [1, 1, 1, 0, 1, 1, 1, 0, 1, 0],
+        [1, 0, 1, 1, 1, 1, 0, 1, 0, 0],
+        [1, 0, 0, 0, 0, 1, 0, 0, 0, 1],
+        [1, 0, 1, 1, 1, 1, 0, 1, 1, 1],
+        [1, 1, 1, 0, 0, 0, 1, 0, 0, 1],
+    ]
+
+    # Define the source and destination
+    src = (8, 0)
+    dest = (0, 0)
+
+    # Run the A* search algorithm
+    a_star_search(grid, src, dest)