diff --git a/dsa-solutions/lc-solutions/0500-0599/0525-Contiguous-Array.md b/dsa-solutions/lc-solutions/0500-0599/0525-Contiguous-Array.md
new file mode 100644
index 000000000..7782b22bf
--- /dev/null
+++ b/dsa-solutions/lc-solutions/0500-0599/0525-Contiguous-Array.md
@@ -0,0 +1,131 @@
+---
+id: contiguous-array
+title: Contiguous Array
+sidebar_label: 0525-Contiguous-Array
+tags:
+- Array
+- Hash Table
+description: "Given a binary array nums, return the maximum length of a contiguous subarray with an equal number of 0 and 1."
+---
+
+## Problem
+
+Given a binary array `nums`, return the maximum length of a contiguous subarray with an equal number of `0` and `1`.
+
+### Examples
+
+**Example 1:**
+
+**Input:** `nums = [0,1]`  
+**Output:** `2`  
+**Explanation:** [0, 1] is the longest contiguous subarray with an equal number of 0 and 1.
+
+**Example 2:**
+
+**Input:** `nums = [0,1,0]`  
+**Output:** `2`  
+**Explanation:** [0, 1] (or [1, 0]) is the longest contiguous subarray with an equal number of 0 and 1.
+
+### Constraints
+
+- `1 <= nums.length <= 10^5`
+- `nums[i]` is either `0` or `1`.
+
+---
+
+## Approach
+
+To solve this problem, we can use a hash map to keep track of the cumulative sum of the transformed array where `0` is replaced by `-1` and `1` is kept as `1`. The idea is to find the subarray with a cumulative sum of zero.
+
+### Steps:
+
+1. Initialize a hash map to store the cumulative sum and its corresponding index.
+2. Initialize `cumulative_sum` to `0` and `max_length` to `0`.
+3. Traverse the array:
+   - Replace `0` with `-1`.
+   - Update the `cumulative_sum`.
+   - If the `cumulative_sum` is `0`, update `max_length` to the current index + 1.
+   - If the `cumulative_sum` has been seen before, calculate the subarray length and update `max_length` if it's larger than the current `max_length`.
+   - If the `cumulative_sum` has not been seen before, store it in the hash map with the current index.
+4. Return `max_length`.
+
+### Solution
+
+#### Java
+
+```java
+import java.util.HashMap;
+
+class Solution {
+    public int findMaxLength(int[] nums) {
+        HashMap<Integer, Integer> map = new HashMap<>();
+        map.put(0, -1);
+        int maxLength = 0, cumulativeSum = 0;
+
+        for (int i = 0; i < nums.length; i++) {
+            cumulativeSum += (nums[i] == 0) ? -1 : 1;
+            if (map.containsKey(cumulativeSum)) {
+                maxLength = Math.max(maxLength, i - map.get(cumulativeSum));
+            } else {
+                map.put(cumulativeSum, i);
+            }
+        }
+
+        return maxLength;
+    }
+}
+```
+#### C++
+
+```cpp
+#include <vector>
+#include <unordered_map>
+using namespace std;
+
+class Solution {
+public:
+    int findMaxLength(vector<int>& nums) {
+        unordered_map<int, int> map;
+        map[0] = -1;
+        int maxLength = 0, cumulativeSum = 0;
+
+        for (int i = 0; i < nums.size(); i++) {
+            cumulativeSum += (nums[i] == 0) ? -1 : 1;
+            if (map.find(cumulativeSum) != map.end()) {
+                maxLength = max(maxLength, i - map[cumulativeSum]);
+            } else {
+                map[cumulativeSum] = i;
+            }
+        }
+
+        return maxLength;
+    }
+};
+```
+#### Python
+
+```python
+class Solution:
+    def findMaxLength(self, nums: List[int]) -> int:
+        count_map = {0: -1}
+        max_length = 0
+        cumulative_sum = 0
+
+        for i, num in enumerate(nums):
+            cumulative_sum += -1 if num == 0 else 1
+            if cumulative_sum in count_map:
+                max_length = max(max_length, i - count_map[cumulative_sum])
+            else:
+                count_map[cumulative_sum] = i
+
+        return max_length
+```
+### Complexity Analysis
+**Time Complexity:** O(n)
+>Reason: We traverse the array once, and each lookup or insertion operation in the hash map is O(1) on average.
+
+**Space Complexity:** O(n)
+>Reason: In the worst case, we may store up to n entries in the hash map.
+
+### References
+**LeetCode Problem:** Contiguous Array
\ No newline at end of file