diff --git a/linear_programming/dual_simplex.py b/linear_programming/dual_simplex.py
new file mode 100644
index 000000000000..67582ceae87b
--- /dev/null
+++ b/linear_programming/dual_simplex.py
@@ -0,0 +1,31 @@
+# Import PuLP library
+from pulp import LpMaximize, LpProblem, LpVariable, LpStatus, value
+
+# Create a Linear Programming Minimization problem
+prob = LpProblem("Dual_Simplex_Example", LpMaximize)
+
+# Create decision variables for the dual problem
+y1 = LpVariable("y1", lowBound=0)  # y1 >= 0
+y2 = LpVariable("y2", lowBound=0)  # y2 >= 0
+y3 = LpVariable("y3", lowBound=0)  # y3 >= 0
+
+# Objective function (minimization in dual corresponds to maximization here)
+prob += 4 * y1 + 2 * y2 + 3 * y3, "Objective"
+
+# Constraints for the dual problem
+prob += y1 + y2 >= 3, "Constraint 1"
+prob += y1 + y3 >= 2, "Constraint 2"
+
+# Solve the problem using the dual simplex method
+prob.solve()
+
+# Print the status of the solution
+print(f"Status: {LpStatus[prob.status]}")
+
+# Print the results (optimal values for y1, y2, and y3)
+print(f"y1 = {value(y1)}")
+print(f"y2 = {value(y2)}")
+print(f"y3 = {value(y3)}")
+
+# Print the objective value (minimized value)
+print(f"Optimal objective value: {value(prob.objective)}")