diff --git a/src/main/java/com/thealgorithms/backtracking/KMeanClustering.java b/src/main/java/com/thealgorithms/backtracking/KMeanClustering.java
new file mode 100644
index 000000000000..d46de6d7ea87
--- /dev/null
+++ b/src/main/java/com/thealgorithms/backtracking/KMeanClustering.java
@@ -0,0 +1,147 @@
+package com.thealgorithms.backtracking;
+
+
+// Author: Jivan Jamdar 
+/*
+K-Means Clustering
+
+Problem Description:
+K-Means clustering is an unsupervised machine learning algorithm used to partition a set of data points into 𝑘
+k distinct clusters based on feature similarity. The algorithm aims to minimize the variance within each cluster while maximizing the variance between clusters.
+
+|| Steps of K-Means Algorithm ||
+
+1) Initialization: Randomly select 𝑘 data points as initial centroids.
+2) Assignment: Assign each data point to the nearest centroid, forming 𝑘 clusters.
+3) Update: Recalculate the centroids of the clusters based on the assigned points.
+4) Repeat: Repeat the assignment and update steps until the centroids no longer change or a maximum number of iterations is reached.
+
+(e.g.)
+Data Points:
+
+(1.0, 2.0)
+(1.5, 1.8)
+(5.0, 8.0)
+(8.0, 8.0)
+(1.0, 0.6)
+(9.0, 11.0)
+(8.0, 2.0)
+(10.0, 2.0)
+(9.0, 3.0)
+K = 2 (Number of clusters)
+
+Initial Centroids (Randomly selected):
+
+Centroid 1: (1.0, 2.0)
+Centroid 2: (5.0, 8.0)
+
+Final Clusters After Execution:
+
+Cluster 1:
+
+(1.0, 2.0)
+(1.5, 1.8)
+(1.0, 0.6)
+(8.0, 2.0)
+(10.0, 2.0)
+(9.0, 3.0)
+
+Cluster 2:
+
+(5.0, 8.0)
+(8.0, 8.0)
+(9.0, 11.0)
+
+*/
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+
+public class KMeans {
+
+    // Method to perform K-Means clustering
+    public List<List<double[]>> kMeans(double[][] data, int k, int maxIterations) {
+        int n = data.length;
+        int dimensions = data[0].length;
+        double[][] centroids = new double[k][dimensions];
+        Random rand = new Random();
+
+        // Step 1: Initialize centroids randomly
+        for (int i = 0; i < k; i++) {
+            centroids[i] = data[rand.nextInt(n)];
+        }
+
+        List<List<double[]>> clusters = new ArrayList<>();
+
+        for (int iteration = 0; iteration < maxIterations; iteration++) {
+            // Step 2: Assign data points to nearest centroid
+            clusters = new ArrayList<>();
+            for (int i = 0; i < k; i++) {
+                clusters.add(new ArrayList<>());
+            }
+
+            for (double[] point : data) {
+                int nearestCentroid = getNearestCentroid(point, centroids);
+                clusters.get(nearestCentroid).add(point);
+            }
+
+            // Step 3: Recalculate centroids
+            boolean centroidsChanged = false;
+            for (int i = 0; i < k; i++) {
+                double[] newCentroid = calculateCentroid(clusters.get(i), dimensions);
+                if (!Arrays.equals(centroids[i], newCentroid)) {
+                    centroids[i] = newCentroid;
+                    centroidsChanged = true;
+                }
+            }
+
+            // Step 4: Stop if centroids didn't change
+            if (!centroidsChanged) {
+                break;
+            }
+        }
+
+        return clusters;
+    }
+
+    // Helper method to calculate the centroid of a cluster
+    private double[] calculateCentroid(List<double[]> cluster, int dimensions) {
+        double[] centroid = new double[dimensions];
+        for (double[] point : cluster) {
+            for (int i = 0; i < dimensions; i++) {
+                centroid[i] += point[i];
+            }
+        }
+        for (int i = 0; i < dimensions; i++) {
+            centroid[i] /= cluster.size();
+        }
+        return centroid;
+    }
+
+    // Helper method to find the nearest centroid for a given point
+    private int getNearestCentroid(double[] point, double[][] centroids) {
+        double minDistance = Double.MAX_VALUE;
+        int nearest = 0;
+
+        for (int i = 0; i < centroids.length; i++) {
+            double distance = calculateDistance(point, centroids[i]);
+            if (distance < minDistance) {
+                minDistance = distance;
+                nearest = i;
+            }
+        }
+
+        return nearest;
+    }
+
+    // Helper method to calculate Euclidean distance between two points
+    private double calculateDistance(double[] point1, double[] point2) {
+        double sum = 0;
+        for (int i = 0; i < point1.length; i++) {
+            sum += Math.pow(point1[i] - point2[i], 2);
+        }
+        return Math.sqrt(sum);
+    }
+}
diff --git a/src/test/java/com/thealgorithms/backtracking/KMeanClusteringTest.java b/src/test/java/com/thealgorithms/backtracking/KMeanClusteringTest.java
new file mode 100644
index 000000000000..68ae1f8fca01
--- /dev/null
+++ b/src/test/java/com/thealgorithms/backtracking/KMeanClusteringTest.java
@@ -0,0 +1,22 @@
+package com.thealgorithms.backtracking;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+import java.util.List;
+import org.junit.jupiter.api.Test;
+
+public class KMeansTest {
+
+    @Test
+    void testSimpleKMeans() {
+        KMeans kMeans = new KMeans();
+
+        double[][] data = {
+            {1.0, 2.0}, {1.5, 1.8}, {5.0, 8.0}, {8.0, 8.0},
+            {1.0, 0.6}, {9.0, 11.0}, {8.0, 2.0}, {10.0, 2.0}, {9.0, 3.0}
+        };
+
+        List<List<double[]>> clusters = kMeans.kMeans(data, 2, 100);
+        assertEquals(2, clusters.size());
+    }
+}