diff --git a/src/machine_learning/loss_function/marginal_ranking.rs b/src/machine_learning/loss_function/marginal_ranking.rs
new file mode 100644
index 00000000000..8661c2d5202
--- /dev/null
+++ b/src/machine_learning/loss_function/marginal_ranking.rs
@@ -0,0 +1,109 @@
+// Marginal Ranking
+//
+// The 'mrg_ranking_loss' function calculates the Marginal Ranking loss, which is a
+// loss function used for ranking problems in machine learning.
+//
+// ## Formula
+//
+// For a pair of values `x_first` and `x_second`, `margin`, and `y_true`,
+// the Marginal Ranking loss is calculated as:
+//
+//  - loss = `max(0, -y_true * (x_first - x_second) + margin)`.
+//
+// It returns the average loss by dividing the `total_loss` by total no. of
+// elements.
+
+pub fn mrg_ranking_loss(
+    x_first: &[f64],
+    x_second: &[f64],
+    margin: f64,
+    y_true: f64,
+) -> Option<f64> {
+    if x_first.len() != x_second.len() || x_first.is_empty() || x_second.is_empty() {
+        return None;
+    }
+    if margin < 0.0 {
+        return None;
+    }
+    if y_true != 1.0 && y_true != -1.0 {
+        return None;
+    }
+
+    let mut total_loss: f64 = 0.0;
+    for (f, s) in x_first.iter().zip(x_second.iter()) {
+        let loss: f64 = (margin - y_true * (f - s)).max(0.0);
+        total_loss += loss;
+    }
+    Some(total_loss / (x_first.len() as f64))
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_marginal_ranking_loss() {
+        let first_values: Vec<f64> = vec![1.0, 2.0, 3.0];
+        let second_values: Vec<f64> = vec![2.0, 3.0, 4.0];
+        let margin: f64 = 1.0;
+        let actual_value: f64 = -1.0;
+        assert_eq!(
+            mrg_ranking_loss(&first_values, &second_values, margin, actual_value),
+            Some(0.0)
+        );
+    }
+
+    #[test]
+    fn test_marginal_ranking_loss_invalid_length0() {
+        let x_first: Vec<f64> = vec![1.0, 2.0, 3.0];
+        let x_second: Vec<f64> = vec![2.0, 3.0];
+        let margin: f64 = 1.0;
+        let y_true: f64 = 1.0;
+        assert_eq!(mrg_ranking_loss(&x_first, &x_second, margin, y_true), None);
+    }
+
+    #[test]
+    fn test_marginal_ranking_loss_invalid_length1() {
+        let x_first: Vec<f64> = vec![1.0, 2.0];
+        let x_second: Vec<f64> = vec![2.0, 3.0, 4.0];
+        let margin: f64 = 1.0;
+        let y_true: f64 = 1.0;
+        assert_eq!(mrg_ranking_loss(&x_first, &x_second, margin, y_true), None);
+    }
+
+    #[test]
+    fn test_marginal_ranking_invalid_values() {
+        let x_first: Vec<f64> = vec![1.0, 2.0, 3.0];
+        let x_second: Vec<f64> = vec![2.0, 3.0, 4.0];
+        let margin: f64 = -1.0;
+        let y_true: f64 = 1.0;
+        assert_eq!(mrg_ranking_loss(&x_first, &x_second, margin, y_true), None);
+    }
+
+    #[test]
+    fn test_marginal_ranking_invalid_y_true() {
+        let x_first: Vec<f64> = vec![1.0, 2.0, 3.0];
+        let x_second: Vec<f64> = vec![2.0, 3.0, 4.0];
+        let margin: f64 = 1.0;
+        let y_true: f64 = 2.0;
+        assert_eq!(mrg_ranking_loss(&x_first, &x_second, margin, y_true), None);
+    }
+
+    #[test]
+    fn test_marginal_ranking_empty_prediction0() {
+        let x_first: Vec<f64> = vec![];
+        let x_second: Vec<f64> = vec![1.0, 2.0, 3.0];
+        let margin: f64 = 1.0;
+        let y_true: f64 = 1.0;
+        assert_eq!(mrg_ranking_loss(&x_first, &x_second, margin, y_true), None);
+    }
+
+    #[test]
+    fn test_marginal_ranking_empty_prediction1() {
+        let x_first: Vec<f64> = vec![1.0, 2.0, 3.0];
+        let x_second: Vec<f64> = vec![];
+        let margin: f64 = 1.0;
+        let y_true: f64 = 1.0;
+        assert_eq!(mrg_ranking_loss(&x_first, &x_second, margin, y_true), None);
+    }
+}
diff --git a/src/machine_learning/loss_function/mod.rs b/src/machine_learning/loss_function/mod.rs
index c0196666dbf..da28315d066 100644
--- a/src/machine_learning/loss_function/mod.rs
+++ b/src/machine_learning/loss_function/mod.rs
@@ -1,11 +1,13 @@
 mod hinge_loss;
 mod huber_loss;
 mod kl_divergence_loss;
+mod marginal_ranking;
 mod mean_absolute_error_loss;
 mod mean_squared_error_loss;
 
 pub use self::hinge_loss::hng_loss;
 pub use self::huber_loss::huber_loss;
 pub use self::kl_divergence_loss::kld_loss;
+pub use self::marginal_ranking::mrg_ranking_loss;
 pub use self::mean_absolute_error_loss::mae_loss;
 pub use self::mean_squared_error_loss::mse_loss;
diff --git a/src/machine_learning/mod.rs b/src/machine_learning/mod.rs
index a88a811e0ba..8d12c3ebca0 100644
--- a/src/machine_learning/mod.rs
+++ b/src/machine_learning/mod.rs
@@ -11,6 +11,7 @@ pub use self::loss_function::hng_loss;
 pub use self::loss_function::huber_loss;
 pub use self::loss_function::kld_loss;
 pub use self::loss_function::mae_loss;
+pub use self::loss_function::mrg_ranking_loss;
 pub use self::loss_function::mse_loss;
 pub use self::optimization::gradient_descent;
 pub use self::optimization::Adam;