diff --git a/machine_learning/clustering/__init__.py b/machine_learning/clustering/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/machine_learning/clustering/hierarchical_clustering.py b/machine_learning/clustering/hierarchical_clustering.py
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+++ b/machine_learning/clustering/hierarchical_clustering.py
@@ -0,0 +1,56 @@
+"""
+Hierarchical Clustering using Euclidean distance and single linkage.
+Reference: https://en.wikipedia.org/wiki/Hierarchical_clustering
+
+>>> import numpy as np
+>>> data = np.array([[0, 0], [1, 1], [5, 5], [6, 6]])
+>>> hc = HierarchicalClustering()
+>>> hc.fit(data)
+>>> hc.clusters
+[[0, 1], [2, 3]]
+"""
+
+import numpy as np
+
+
+class HierarchicalClustering:
+    def __init__(self) -> None:
+        """Initialize the clustering model."""
+        self.clusters: list[list[int]] = []
+
+    def euclidean_distance(self, point_a: np.ndarray, point_b: np.ndarray) -> float:
+        """Compute Euclidean distance between two points."""
+        return np.linalg.norm(point_a - point_b)
+
+    def fit(self, data: np.ndarray) -> None:
+        """
+        Perform hierarchical clustering using single linkage.
+
+        Args:
+            data: 2D numpy array of shape (n_samples, n_features)
+        """
+        n_samples = len(data)
+        clusters = [[i] for i in range(n_samples)]
+
+        while len(clusters) > 2:
+            min_dist = float("inf")
+            to_merge = (0, 1)
+
+            for i in range(len(clusters)):
+                for j in range(i + 1, len(clusters)):
+                    dist = min(
+                        self.euclidean_distance(data[p1], data[p2])
+                        for p1 in clusters[i]
+                        for p2 in clusters[j]
+                    )
+                    if dist < min_dist:
+                        min_dist = dist
+                        to_merge = (i, j)
+
+            merged = clusters[to_merge[0]] + clusters[to_merge[1]]
+            clusters = [
+                c for idx, c in enumerate(clusters) if idx not in to_merge
+            ]
+            clusters.append(merged)
+
+        self.clusters = clusters