diff --git a/src/main/java/com/thealgorithms/machinelearning/MultinomialNaiveBayesClassifier.java b/src/main/java/com/thealgorithms/machinelearning/MultinomialNaiveBayesClassifier.java
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+++ b/src/main/java/com/thealgorithms/machinelearning/MultinomialNaiveBayesClassifier.java
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+package com.thealgorithms.machinelearning;
+
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * Multinomial Naive Bayes classifier.
+ *
+ * <p>Suited to discrete, count-based features (e.g. word frequencies in text
+ * classification). Class priors and feature likelihoods are estimated from
+ * training data with Laplace (add-alpha) smoothing to avoid zero
+ * probabilities for unseen feature/class combinations. Predictions are made
+ * by comparing summed log-probabilities across classes, which avoids the
+ * numerical underflow that repeated multiplication of small probabilities
+ * would cause.
+ *
+ * <p>Reference: <a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier">
+ * Naive Bayes classifier</a>
+ *
+ * @author Vraj Prajapati(Rosander0)
+ */
+public final class MultinomialNaiveBayesClassifier {
+
+    private final double alpha;
+    private final Map<Integer, Double> logPriors;
+    private final Map<Integer, double[]> logLikelihoods;
+    private int numFeatures;
+
+    /**
+     * Constructs a classifier with the given Laplace smoothing parameter.
+     *
+     * @param alpha smoothing constant; must be greater than 0. A value of 1.0
+     *              corresponds to standard Laplace smoothing.
+     */
+    public MultinomialNaiveBayesClassifier(double alpha) {
+        if (alpha <= 0) {
+            throw new IllegalArgumentException("alpha must be greater than 0");
+        }
+        this.alpha = alpha;
+        this.logPriors = new HashMap<>();
+        this.logLikelihoods = new HashMap<>();
+    }
+
+    /** Constructs a classifier using the standard Laplace smoothing constant of 1.0. */
+    public MultinomialNaiveBayesClassifier() {
+        this(1.0);
+    }
+
+    /**
+     * Fits the classifier on the given feature matrix and labels.
+     *
+     * @param features training samples, each row a vector of non-negative
+     *                 feature counts
+     * @param labels   class label for each row of {@code features}
+     */
+    public void fit(double[][] features, int[] labels) {
+        if (features.length == 0 || features.length != labels.length) {
+            throw new IllegalArgumentException("features and labels must be non-empty and of equal length");
+        }
+        numFeatures = features[0].length;
+
+        Map<Integer, Integer> classCounts = new HashMap<>();
+        Map<Integer, double[]> featureSums = new HashMap<>();
+        Map<Integer, Double> totalFeatureCount = new HashMap<>();
+
+        for (int i = 0; i < features.length; i++) {
+            int label = labels[i];
+            classCounts.merge(label, 1, Integer::sum);
+            double[] sums = featureSums.computeIfAbsent(label, k -> new double[numFeatures]);
+            double total = totalFeatureCount.getOrDefault(label, 0.0);
+            for (int j = 0; j < numFeatures; j++) {
+                sums[j] += features[i][j];
+                total += features[i][j];
+            }
+            totalFeatureCount.put(label, total);
+        }
+
+        int totalSamples = features.length;
+        for (Map.Entry<Integer, double[]> entry : featureSums.entrySet()) {
+            int label = entry.getKey();
+            double[] sums = entry.getValue();
+            int count = classCounts.getOrDefault(label, 0);
+            double total = totalFeatureCount.getOrDefault(label, 0.0);
+
+            logPriors.put(label, Math.log((double) count / totalSamples));
+
+            double denom = total + alpha * numFeatures;
+            double[] logLikelihood = new double[numFeatures];
+            for (int j = 0; j < numFeatures; j++) {
+                logLikelihood[j] = Math.log((sums[j] + alpha) / denom);
+            }
+            logLikelihoods.put(label, logLikelihood);
+        }
+    }
+
+    /**
+     * Predicts the most likely class for a single sample.
+     *
+     * @param sample feature vector of non-negative counts
+     * @return the predicted class label
+     */
+    public int predict(double[] sample) {
+        if (logPriors.isEmpty()) {
+            throw new IllegalStateException("classifier has not been fitted");
+        }
+        if (sample.length != numFeatures) {
+            throw new IllegalArgumentException("sample length must match training feature count");
+        }
+
+        int bestLabel = -1;
+        double bestScore = Double.NEGATIVE_INFINITY;
+
+        for (Map.Entry<Integer, double[]> entry : logLikelihoods.entrySet()) {
+            int label = entry.getKey();
+            double[] logLikelihood = entry.getValue();
+            double score = logPriors.getOrDefault(label, Double.NEGATIVE_INFINITY);
+            for (int j = 0; j < numFeatures; j++) {
+                score += sample[j] * logLikelihood[j];
+            }
+            if (score > bestScore) {
+                bestScore = score;
+                bestLabel = label;
+            }
+        }
+        return bestLabel;
+    }
+
+    /**
+     * Predicts class labels for a batch of samples.
+     *
+     * @param samples feature vectors of non-negative counts
+     * @return predicted class label for each row of {@code samples}
+     */
+    public int[] predict(double[][] samples) {
+        int[] predictions = new int[samples.length];
+        for (int i = 0; i < samples.length; i++) {
+            predictions[i] = predict(samples[i]);
+        }
+        return predictions;
+    }
+}
diff --git a/src/test/java/com/thealgorithms/machinelearning/MultinomialNaiveBayesClassifierTest.java b/src/test/java/com/thealgorithms/machinelearning/MultinomialNaiveBayesClassifierTest.java
new file mode 100644
index 000000000000..fa268d3bd44a
--- /dev/null
+++ b/src/test/java/com/thealgorithms/machinelearning/MultinomialNaiveBayesClassifierTest.java
@@ -0,0 +1,116 @@
+package com.thealgorithms.machinelearning;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import org.junit.jupiter.api.Test;
+
+class MultinomialNaiveBayesClassifierTest {
+
+    @Test
+    void predictsCorrectClassOnSeparableToyDataset() {
+        // Class 0 samples are dominated by feature 0; class 1 samples by feature 1.
+        double[][] features = {
+            {5, 1},
+            {6, 0},
+            {4, 1},
+            {1, 5},
+            {0, 6},
+            {1, 4},
+        };
+        int[] labels = {0, 0, 0, 1, 1, 1};
+
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
+        classifier.fit(features, labels);
+
+        assertEquals(0, classifier.predict(new double[] {5, 0}));
+        assertEquals(1, classifier.predict(new double[] {0, 5}));
+    }
+
+    @Test
+    void predictBatchMatchesIndividualPredictions() {
+        double[][] features = {
+            {3, 0},
+            {2, 0},
+            {0, 3},
+            {0, 2},
+        };
+        int[] labels = {0, 0, 1, 1};
+
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
+        classifier.fit(features, labels);
+
+        double[][] samples = {{4, 0}, {0, 4}};
+        int[] predictions = classifier.predict(samples);
+
+        assertEquals(classifier.predict(samples[0]), predictions[0]);
+        assertEquals(classifier.predict(samples[1]), predictions[1]);
+    }
+
+    @Test
+    void laplaceSmoothingKeepsZeroCountFeatureLogProbabilityFinite() {
+        // Feature index 1 never appears for class 0 in training data.
+        double[][] features = {
+            {2, 0},
+            {3, 0},
+            {0, 2},
+            {0, 3},
+        };
+        int[] labels = {0, 0, 1, 1};
+
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
+        classifier.fit(features, labels);
+
+        // A sample that hits class 0's zero-count feature should still produce
+        // a finite, usable prediction instead of -Infinity collapsing the score.
+        int prediction = classifier.predict(new double[] {1, 1});
+        assertTrue(prediction == 0 || prediction == 1);
+    }
+
+    @Test
+    void predictBeforeFitThrowsIllegalStateException() {
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
+        assertThrows(IllegalStateException.class, () -> classifier.predict(new double[] {1, 2}));
+    }
+
+    @Test
+    void nonPositiveAlphaThrowsIllegalArgumentException() {
+        assertThrows(IllegalArgumentException.class, () -> new MultinomialNaiveBayesClassifier(0.0));
+        assertThrows(IllegalArgumentException.class, () -> new MultinomialNaiveBayesClassifier(-1.0));
+    }
+
+    @Test
+    void mismatchedSampleLengthThrowsIllegalArgumentException() {
+        double[][] features = {
+            {1, 2},
+            {3, 4},
+        };
+        int[] labels = {0, 1};
+
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
+        classifier.fit(features, labels);
+
+        assertThrows(IllegalArgumentException.class, () -> classifier.predict(new double[] {1, 2, 3}));
+    }
+
+    @Test
+    void mismatchedFeatureAndLabelLengthsThrowsIllegalArgumentException() {
+        double[][] features = {
+            {1, 2},
+            {3, 4},
+        };
+        int[] labels = {0};
+
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
+        assertThrows(IllegalArgumentException.class, () -> classifier.fit(features, labels));
+    }
+
+    @Test
+    void emptyFeaturesArrayThrowsIllegalArgumentException() {
+        double[][] features = {};
+        int[] labels = {};
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
+        assertThrows(IllegalArgumentException.class, () -> classifier.fit(features, labels));
+    }
+}