diff --git a/algorithms-modules/algorithms-numeric/src/main/java/com/baeldung/algorithms/fastgaussianblur/FastGaussianBlur.java b/algorithms-modules/algorithms-numeric/src/main/java/com/baeldung/algorithms/fastgaussianblur/FastGaussianBlur.java
new file mode 100644
index 000000000000..42b2ef10ba2c
--- /dev/null
+++ b/algorithms-modules/algorithms-numeric/src/main/java/com/baeldung/algorithms/fastgaussianblur/FastGaussianBlur.java
@@ -0,0 +1,100 @@
+package com.baeldung.algorithms.fastgaussianblur;
+
+/**
+ * Fast Gaussian Blur approximation using sliding window over rows and columns
+ */
+public class FastGaussianBlur {
+    /**
+     * Horizontal box blur over rows
+     *
+     * @param source Source image row.
+     * @param target Target image row.
+     * @param width Image width.
+     * @param height Image height.
+     * @param radius Blur radius.
+     */
+    private static void horizontalBoxBlur(int[] source, int[] target, int width, int height, int radius) {
+        double scale = 1.0 / (radius * 2 + 1);
+
+        for (int y = 0; y < height; y++) {
+            int windowSum = 0;
+            int offset = y * width;
+
+            // 1. We initialize the sliding window for the first pixel in the row
+            for (int x = -radius; x <= radius; x++) {
+                int safeX = Math.min(Math.max(x, 0), width - 1);
+                windowSum += source[offset + safeX];
+            }
+
+            // 2. We slide the window across the row
+            for (int x = 0; x < width; x++) {
+                target[offset + x] = (int) Math.round(windowSum * scale);
+
+                // 2a. We subtract the leaving pixel and add the entering pixel
+                int leftX = Math.max(x - radius, 0);
+                int rightX = Math.min(x + radius + 1, width - 1);
+
+                // 2b. We update the sliding window
+                windowSum -= source[offset + leftX];
+                windowSum += source[offset + rightX];
+            }
+        }
+    }
+
+    /**
+     * Vertical box blur over columns. It is identical in logic, but we just
+     * traverse columns instead of rows
+     *
+     * @param source Source image row.
+     * @param target Target image row.
+     * @param width Image width.
+     * @param height Image height.
+     * @param radius Blur radius.
+     */
+    private static void verticalBoxBlur(int[] source, int[] target, int width, int height, int radius) {
+        double scale = 1.0 / (radius * 2 + 1);
+
+        for (int x = 0; x < width; x++) {
+            int windowSum = 0;
+
+            for (int y = -radius; y <= radius; y++) {
+                int safeY = Math.min(Math.max(y, 0), height - 1);
+                windowSum += source[safeY * width + x];
+            }
+
+            for (int y = 0; y < height; y++) {
+                target[y * width + x] = (int) Math.round(windowSum * scale);
+
+                int topY = Math.max(y - radius, 0);
+                int bottomY = Math.min(y + radius + 1, height - 1);
+
+                windowSum -= source[topY * width + x];
+                windowSum += source[bottomY * width + x];
+            }
+        }
+    }
+
+    /**
+     * Main orchestrator to call Fast Gaussian 2D Blur by separating it into
+     * two 1D operations
+     * @param source Source image row.
+     * @param width Image width
+     * @param height Image height
+     * @param radius Blur radius
+     * @param numPasses Number of Passes to Gaussian Approximate
+     */
+    public static int[] applyFastGaussianBlur(int[] source, int width, int height, int radius, int numPasses) {
+        int[] target = new int[source.length];
+        int[] temp = new int[source.length];
+
+        // 1. Copy original image data to target
+        System.arraycopy(source, 0, target, 0, source.length);
+
+        // 2. Run (numPasses = 5) iterations for the Gaussian approximation
+        for (int i = 0; i < numPasses; i++) {
+            horizontalBoxBlur(target, temp, width, height, radius);
+            verticalBoxBlur(temp, target, width, height, radius);
+        }
+        return target;
+    }
+}
diff --git a/algorithms-modules/algorithms-numeric/src/main/java/com/baeldung/algorithms/fastgaussianblur/FastGaussianBlurRealImageTester.java b/algorithms-modules/algorithms-numeric/src/main/java/com/baeldung/algorithms/fastgaussianblur/FastGaussianBlurRealImageTester.java
new file mode 100644
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--- /dev/null
+++ b/algorithms-modules/algorithms-numeric/src/main/java/com/baeldung/algorithms/fastgaussianblur/FastGaussianBlurRealImageTester.java
@@ -0,0 +1,89 @@
+package com.baeldung.algorithms.fastgaussianblur;
+
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.InputStream;
+import javax.imageio.ImageIO;
+
+import javax.annotation.Nonnull;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * Fast Gaussian Blur approximation tester on a real RGB image
+ */
+public class FastGaussianBlurRealImageTester {
+
+    private static final Logger LOGGER = LoggerFactory.getLogger(FastGaussianBlurRealImageTester.class);
+
+    @Nonnull
+    public static BufferedImage blurRealImage(@Nonnull BufferedImage image, int radius, int numPasses) {
+        int width = image.getWidth();
+        int height = image.getHeight();
+
+        // 1. We extract 1D array of 32-bit ARGB pixels
+        int[] pixels = image.getRGB(0, 0, width, height, null, 0, width);
+
+        // 2. We define arrays to hold independent color channels
+        int[] a = new int[pixels.length];
+        int[] r = new int[pixels.length];
+        int[] g = new int[pixels.length];
+        int[] b = new int[pixels.length];
+
+        // 3. We unpack the 32-bit integers into separate channels
+        for (int i = 0; i < pixels.length; i++) {
+            a[i] = (pixels[i] >> 24) & 0xff;
+            r[i] = (pixels[i] >> 16) & 0xff;
+            g[i] = (pixels[i] >> 8) & 0xff;
+            b[i] = pixels[i] & 0xff;
+        }
+
+        // 4. We apply our O(n) FastGaussianBlur algorithm to each color channel independently
+        r = FastGaussianBlur.applyFastGaussianBlur(r, width, height, radius, numPasses);
+        g = FastGaussianBlur.applyFastGaussianBlur(g, width, height, radius, numPasses);
+        b = FastGaussianBlur.applyFastGaussianBlur(b, width, height, radius, numPasses);
+
+        // 5. We repack the channels back into a 32-bit integer array
+        int[] resultPixels = new int[pixels.length];
+        for (int i = 0; i < pixels.length; i++) {
+            resultPixels[i] = (a[i] << 24) | (r[i] << 16) | (g[i] << 8) | b[i];
+        }
+
+        // 6. We create a new BufferedImage and set the blurred pixels
+        BufferedImage result = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
+        result.setRGB(0, 0, width, height, resultPixels, 0, width);
+
+        // 7. We return the result
+        return result;
+    }
+
+    public static void main(String[] args) throws Exception {
+        long startTime = System.currentTimeMillis();
+        int radius = 1;
+        int numPasses = 5;
+
+        // 1. We create a thread and read sapmple.jpg from ../src/man/resources
+        InputStream is = Thread.currentThread()
+            .getContextClassLoader()
+            .getResourceAsStream("sample.jpg");
+
+        if (is == null) {
+            throw new RuntimeException("Resource not found: sample.jpg");
+        }
+
+        BufferedImage originalImage = ImageIO.read(is);
+        assert originalImage != null;
+
+        // 2. We run our Fast Blur algorithm
+        BufferedImage blurredImage = blurRealImage(originalImage, radius, numPasses); // Radius 10
+        long endTime = System.currentTimeMillis();
+
+        LOGGER.debug("Blur completed in: " + (endTime - startTime) + " ms");
+
+        //3. We save result image sample_blurred.jpg under algorithms-numeric/target
+        File outputFile = new File("algorithms-numeric/target/sample_blurred.jpg");
+        boolean status = ImageIO.write(blurredImage, "png", outputFile);
+        LOGGER.debug("Blur Operation: " + status + " Saved to: " + outputFile.getAbsolutePath());
+    }
+}
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diff --git a/algorithms-modules/algorithms-numeric/src/main/resources/sample.jpg b/algorithms-modules/algorithms-numeric/src/main/resources/sample.jpg
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diff --git a/algorithms-modules/algorithms-numeric/src/test/java/com/baeldung/algorithms/fastgaussianblur/FastGaussianBlurUnitTest.java b/algorithms-modules/algorithms-numeric/src/test/java/com/baeldung/algorithms/fastgaussianblur/FastGaussianBlurUnitTest.java
new file mode 100644
index 000000000000..aa8c6ac83c42
--- /dev/null
+++ b/algorithms-modules/algorithms-numeric/src/test/java/com/baeldung/algorithms/fastgaussianblur/FastGaussianBlurUnitTest.java
@@ -0,0 +1,32 @@
+package com.baeldung.algorithms.fastgaussianblur;
+import org.junit.jupiter.api.Test;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+
+/**
+ * Fast Gaussian Blur JUnit Test case
+ */
+class FastGaussianBlurUnitTest {
+    @Test
+    void givenSharpImage_whenAppliedBlur_thenCenterIsSmoothed() {
+        int width = 5;
+        int height = 5;
+        int numPasses = 5;
+        int[] image = new int[width * height];
+
+        // 1. We create an impulse image with all indices black image except one bright white pixel in the center.
+        image[12] = 255;
+
+        int[] blurredImage = FastGaussianBlur.applyFastGaussianBlur(image, width, height, 1, numPasses);
+
+        // 2. We expect the center pixel should lose intensity
+        // as it gets spread to its neighbors
+        assertTrue(blurredImage[12] < 255);
+        assertTrue(blurredImage[12] > 0);
+
+        // 3. We expect that its immediate neighbors should have
+        // gained intensity
+        assertTrue(blurredImage[11] > 0); // Left neighbor
+        assertTrue(blurredImage[13] > 0); // Right neighbor
+    }
+}
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