diff --git a/dynamic_programming/optimal_bst.cpp b/dynamic_programming/optimal_bst.cpp
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+/**
+ * @file
+ * @brief Implementation of [Optimal Binary Search Tree (OBST)]
+ * (https://www.geeksforgeeks.org/dynamic-programming-set-24-optimal-binary-search-tree/)
+ *
+ * @details
+ * Given a sorted array of keys and their corresponding frequencies,
+ * the task is to construct a binary search tree with minimum cost.
+ * The cost of a BST node is defined as frequency multiplied by depth.
+ *
+ * ### Algorithm
+ * This uses dynamic programming with prefix sum optimization to reduce
+ * redundant computation. It builds up the solution for increasing subtree
+ * sizes. Time complexity is O(n^3) due to three nested loops.
+ *
+ * @author [Satya Pradhan](https://github.com/butterpaneermasala)
+ */
+
+#include <cassert>
+#include <climits>
+#include <iostream>
+#include <vector>
+
+/**
+ * @namespace dynamic_programming
+ * @brief Dynamic Programming algorithms
+ */
+namespace dynamic_programming {
+/**
+ * @namespace obst
+ * @brief Implementation of Optimal Binary Search Tree
+ */
+namespace obst {
+
+/**
+ * @brief Function to compute the cost of an Optimal Binary Search Tree
+ *
+ * @param freq Vector of frequencies for sorted keys
+ * @return Minimum search cost for OBST
+ */
+int optimalBST(const std::vector<int>& freq) {
+    int n = freq.size();
+    if (n == 0)
+        return 0;
+
+    // dp[i][j] will hold the minimum cost of OBST that can be built from keys i
+    // to j
+    std::vector<std::vector<int>> dp(n, std::vector<int>(n, 0));
+
+    // prefix sum to quickly compute the total frequency from i to j
+    std::vector<int> prefix_sum(n + 1, 0);
+    for (int i = 0; i < n; ++i) {
+        prefix_sum[i + 1] = prefix_sum[i] + freq[i];
+    }
+
+    // Cost of a single key is its frequency (base case)
+    for (int i = 0; i < n; ++i) {
+        dp[i][i] = freq[i];
+    }
+
+    // Build cost for chains of length 2 to n
+    for (int length = 2; length <= n; ++length) {
+        for (int i = 0; i <= n - length; ++i) {
+            int j = i + length - 1;
+            dp[i][j] = INT_MAX;
+
+            int fsum = prefix_sum[j + 1] - prefix_sum[i];
+
+            // Try all keys in [i..j] as root
+            for (int r = i; r <= j; ++r) {
+                int cost = (r > i ? dp[i][r - 1] : 0) +
+                           (r < j ? dp[r + 1][j] : 0) + fsum;
+                dp[i][j] = std::min(dp[i][j], cost);
+            }
+        }
+    }
+
+    return dp[0][n - 1];
+}
+}  // namespace obst
+}  // namespace dynamic_programming
+
+/**
+ * @brief Function to test the above algorithm
+ * @returns void
+ */
+void test() {
+    using dynamic_programming::obst::optimalBST;
+
+    std::vector<int> test1 = {34, 10, 8, 50};
+    assert(optimalBST(test1) == 142);
+
+    std::vector<int> test2 = {10, 12};
+    assert(optimalBST(test2) == 32);
+
+    std::vector<int> test3 = {10, 12, 20};
+    assert(optimalBST(test3) == 82);
+
+    std::vector<int> test4 = {25, 10, 20};
+    assert(optimalBST(test4) == 95);
+
+    std::vector<int> test5 = {4, 2, 6, 3};
+    assert(optimalBST(test5) == 40);
+
+    std::vector<int> test_single = {42};
+    assert(optimalBST(test_single) == 42);
+
+    std::vector<int> test_empty;
+    assert(optimalBST(test_empty) == 0);
+
+    std::cout << "All test cases passed!\n";
+}
+
+/**
+ * @brief Main function
+ * @returns 0 on success
+ */
+int main() {
+    test();
+    return 0;
+}