diff --git a/examples/methods/neighbors/multinomial_nb.cpp b/examples/methods/neighbors/multinomial_nb.cpp
new file mode 100644
index 0000000..ea448c5
--- /dev/null
+++ b/examples/methods/neighbors/multinomial_nb.cpp
@@ -0,0 +1,23 @@
+// #include "../../src/slowmokit/methods/neighbors/multinomial_nb.hpp"
+// #include "../../src/slowmokit/core.hpp"
+
+// signed main(){
+//     std::vector<std::vector<int>> xTrain{
+//         {0,0,0},
+//         {1,0,0},
+//         {1,0,1},
+//         {1,0,0},
+//         {0,1,1},
+//         {1,0,0},
+//         {1,1,0},
+//         {0,1,1},
+//         {1,0,1},
+//         {1,1,1}
+//     };
+//     std::vector<int> yTrain{2,2,2,2,1,2,2,1,2,0};
+//     std::vector<int> xTest{0,0,0};
+//     std::vector<int> classes{0,1,2};
+//     MultinomialNB<int> multinomial;
+//     std::cout<<multinomial.predict(xTrain,yTrain,classes,xTest);
+//     return 0;
+// }
\ No newline at end of file
diff --git a/src/slowmokit.hpp b/src/slowmokit.hpp
index 6b8ac82..0c72ec2 100644
--- a/src/slowmokit.hpp
+++ b/src/slowmokit.hpp
@@ -20,5 +20,6 @@
 #include "slowmokit/methods/neighbors/bernoulli_nb.hpp"
 #include "slowmokit/methods/neighbors/gaussian_nb.hpp"
 #include "slowmokit/methods/neighbors/knn.hpp"
+#include "slowmokit/methods/neighbors/multinomial_nb.hpp"
 
 #endif // SLOWMOKIT_HPP
diff --git a/src/slowmokit/methods/neighbors/multinomial_nb.hpp b/src/slowmokit/methods/neighbors/multinomial_nb.hpp
new file mode 100644
index 0000000..6c11eb3
--- /dev/null
+++ b/src/slowmokit/methods/neighbors/multinomial_nb.hpp
@@ -0,0 +1,13 @@
+/**
+ * @file methods/neighbors/multinomial_nb.hpp
+ *
+ * Easy include for Multinomial Nb algorithm
+ */
+
+
+#ifndef SLOWMOKIT_MULTINOMIAL_NB_HPP
+#define SLOWMOKIT_MULTINOMIAL_NB_HPP
+
+#include "multinomial_nb/multinomial_nb.hpp"
+
+#endif // SLOWMOKIT_MULTINOMIAL_NB_HPP
\ No newline at end of file
diff --git a/src/slowmokit/methods/neighbors/multinomial_nb/multinomial_nb.cpp b/src/slowmokit/methods/neighbors/multinomial_nb/multinomial_nb.cpp
new file mode 100644
index 0000000..d646ccf
--- /dev/null
+++ b/src/slowmokit/methods/neighbors/multinomial_nb/multinomial_nb.cpp
@@ -0,0 +1,96 @@
+/**
+ * @file methods/neighbors/multinomial_nb/multinomial_nb.cpp
+ *
+ * Implementation of the Multinomial Naive Bayes main program
+ */
+#include "multinomial_nb.hpp"
+
+template<class T>
+double MultinomialNB<T>::priorProb(std::vector<T> yTrain, int label)
+{ // Prior-Probability P(y)
+  int sum = 0;
+  for (int i = 0; i < yTrain.size(); i++)
+  {
+    if (yTrain[i] == label)
+    {
+      sum += 1;
+    }
+  }
+  return sum / double(yTrain.size());
+}
+
+template<class T>
+double MultinomialNB<T>::conditionalProb(std::vector<std::vector<T>> xTrain,
+                                         std::vector<T> yTrain, int featureCol,
+                                         int featureVal, int label)
+{ // Conditional-Probabilty P(x=f1 / y=class)
+  int denominator = 0, numerator = 0;
+  std::set<int> varies_values;
+  for (int i = 0; i < yTrain.size(); i++)
+  {
+    if (yTrain[i] == label)
+    {                // Check if label of feature is same as output looking for
+      denominator++; // All values with output_class we are looking for
+      varies_values.insert(xTrain[i][featureCol]);
+      if (xTrain[i][featureCol] == featureVal)
+      {
+        numerator++; // numerator increase only when that feature consists of
+                     // value same as value in testing feature
+      }
+    }
+  }
+  return (numerator + 1) /
+         double(denominator +
+                varies_values
+                    .size()); // as it is multinomial nb so to avoid overfitting
+  // numerator+1 -> because if word do not exist in training but exist in
+  // testing denominator + number_of_different_values_in_feature
+}
+
+template<class T>
+int MultinomialNB<T>::fit_predict(std::vector<std::vector<T>> xTrain,
+                                  std::vector<T> yTrain, std::vector<T> classes,
+                                  std::vector<T> xTest)
+{
+  int n_features = xTrain[0].size();
+
+  std::vector<double> postProbs;
+
+  std::vector<int>::iterator label; // different ouput labels
+  for (label = classes.begin(); label != classes.end(); label++)
+  { // iterating over all labels (P(y=y1,y2,y3....))
+    double likelihood = 1.0;
+    for (int i = 0; i < n_features; i++)
+    {
+      double cond = conditionalProb(xTrain, yTrain, i, xTest[i], *label);
+      likelihood += log10(cond); // Log10 of computed conditionl probabilty =>
+                                 // Log10(P(x=x1 / y=label))
+    }
+
+    double prior = priorProb(yTrain, *label);
+    double post = prior * likelihood;
+    postProbs.push_back(post);
+  }
+  double sumpropProbs = 0.0;
+  int max = 0;
+  for (int i = 0; i < postProbs.size(); i++)
+  { // Iterating over all labels computed values to check if any negative value
+    // exists, if so then direclty values are compared else we will compute
+    // value/sum
+    if (postProbs[i] < 0)
+    {
+      sumpropProbs = 1;
+      break;
+    }
+    sumpropProbs += postProbs[i];
+  }
+  for (int i = 0; i < postProbs.size(); i++)
+  {
+    postProbs[i] /= sumpropProbs;
+    if (postProbs[i] > postProbs[max])
+    {
+      max = i;
+    }
+  }
+  return max;
+};
\ No newline at end of file
diff --git a/src/slowmokit/methods/neighbors/multinomial_nb/multinomial_nb.hpp b/src/slowmokit/methods/neighbors/multinomial_nb/multinomial_nb.hpp
new file mode 100644
index 0000000..04df347
--- /dev/null
+++ b/src/slowmokit/methods/neighbors/multinomial_nb/multinomial_nb.hpp
@@ -0,0 +1,51 @@
+/**
+ * @file methods/neighbors/multinomial_nb/multinomial_nb.hpp
+ *
+ * The header file including the Multinomial Naive Bayes algorithm
+ */
+
+#ifndef SLOWMOKIT_MULTINOMIAL_NB_HPP
+#define SLOWMOKIT_MULTINOMIAL_NB_HPP
+
+#include "../../../core.hpp"
+
+template<class T> class MultinomialNB
+{
+  private:
+  /**
+   * @brief Prior probability of multinomial naive bayes
+   *
+   * @param yTrain All y training values
+   * @param label output we are looking in prior probability
+   * @return double prior Probability
+   */
+  double priorProb(std::vector<T> yTrain, int label);
+  /**
+   * @brief Conditional Probability
+   *
+   * @param xTrain all x training int values
+   * @param yTrain all y training int values
+   * @param featureCol column for which we are computing conditional prob
+   * @param featureVal value in that column
+   * @param label y value
+   * @return double conditional probability
+   */
+  double conditionalProb(std::vector<std::vector<T>> xTrain,
+                         std::vector<T> yTrain, int featureCol, int featureVal,
+                         int label);
+
+  public:
+  /**
+   * @brief fit_predict function
+   *
+   * @param xTrain all x training values
+   * @param yTrain all y training values int
+   * @param classes classes of y
+   * @param xTest testing values int
+   * @return int output predicted value
+   */
+  int fit_predict(std::vector<std::vector<T>> xTrain, std::vector<T> yTrain,
+                  std::vector<T> classes, std::vector<T> xTest);
+};
+
+#endif
\ No newline at end of file