diff --git a/CHANGELOG.md b/CHANGELOG.md
index 32229ec..512b4b3 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,13 +15,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - X10D: Added `IntrinsicExtensions` and `IntrinsicUtility` which offer methods for vectors in `System.Runtime.Instrinsics`. (#70)
 - X10D: Added `MathUtility.Bias(float, float)` and `MathUtility.Bias(double, double)`.
 - X10D: Added `MathUtility.ExponentialDecay(float, float, float)` and `MathUtility.ExponentialDecay(double, double, double)`.
-- X10D: Added `MathUtility.InverseLerp(float, float, float)` and `MathUtility.InverseLerp(double, double, double)`
+- X10D: Added `MathUtility.InverseLerp(float, float, float)` and `MathUtility.InverseLerp(double, double, double)`.
 - X10D: Added `MathUtility.ScaleRange(float, float, float, float, float)`
   and `MathUtility.ScaleRange(double, double, double, double, double)`
-- X10D:
+- X10D: Added `MathUtility.Sigmoid(float)` and `MathUtility.Sigmoid(double)`.
 - X10D: Added `MathUtility.SmoothStep(float, float, float)` and `MathUtility.SmoothStep(double, double, double)`.
-  Added `Circle`, `CircleF`, `Cuboid`, `Ellipse`, `EllipseF`, `Line3D`, `Line`, `LineF`, `Polygon`, `PolygonF`, `Polyhedron`,
-  and `Sphere`, to complement System.Drawing structs such as `Point` and `Rectangle`
+- X10D: Added `Circle`, `CircleF`, `Cuboid`, `Ellipse`, `EllipseF`, `Line3D`, `Line`, `LineF`, `Polygon`, `PolygonF`, `Polyhedron`,
+  and `Sphere`, to complement System.Drawing structs such as `Point` and `Rectangle`.
 - X10D: Added `Color.Deconstruct()` - with optional alpha parameter.
 - X10D: Added `Color.GetClosestConsoleColor()`.
 - X10D: Added `DateTime.GetIso8601WeekOfYear()` and `DateTimeOffset.GetIso8601WeekOfYear()`.
diff --git a/X10D.Tests/src/Math/MathUtilityTests.cs b/X10D.Tests/src/Math/MathUtilityTests.cs
index 2834643..45c37ab 100644
--- a/X10D.Tests/src/Math/MathUtilityTests.cs
+++ b/X10D.Tests/src/Math/MathUtilityTests.cs
@@ -177,6 +177,95 @@ public class MathUtilityTests
         Assert.AreEqual(7.5f, result);
     }
 
+    [TestMethod]
+    public void Sigmoid_ReturnsExpectedValue_UsingDouble()
+    {
+        const double input = 0.5f;
+        const double expected = 0.622459331f;
+
+        double actual = MathUtility.Sigmoid(input);
+
+        Assert.AreEqual(expected, actual, 1e-6);
+    }
+
+    [TestMethod]
+    public void Sigmoid_ReturnsExpectedValue_UsingSingle()
+    {
+        const float input = 0.5f;
+        const float expected = 0.622459331f;
+
+        float actual = MathUtility.Sigmoid(input);
+
+        Assert.AreEqual(expected, actual, 1e-6f);
+    }
+
+    [TestMethod]
+    public void Sigmoid_ReturnsZeroWhenInputIsNegativeInfinity_UsingDouble()
+    {
+        const double input = double.NegativeInfinity;
+        const double expected = 0f;
+
+        double actual = MathUtility.Sigmoid(input);
+
+        Assert.AreEqual(expected, actual, 1e-6);
+    }
+
+    [TestMethod]
+    public void Sigmoid_ReturnsZeroWhenInputIsNegativeInfinity_UsingSingle()
+    {
+        const float input = float.NegativeInfinity;
+        const float expected = 0f;
+
+        float actual = MathUtility.Sigmoid(input);
+
+        Assert.AreEqual(expected, actual, 1e-6f);
+    }
+
+    [TestMethod]
+    public void Sigmoid_ReturnsOneWhenInputIsPositiveInfinity_UsingDouble()
+    {
+        const double input = double.PositiveInfinity;
+        const double expected = 1f;
+
+        double actual = MathUtility.Sigmoid(input);
+
+        Assert.AreEqual(expected, actual, 1e-6);
+    }
+
+    [TestMethod]
+    public void Sigmoid_ReturnsOneWhenInputIsPositiveInfinity_UsingSingle()
+    {
+        const float input = float.PositiveInfinity;
+        const float expected = 1f;
+
+        float actual = MathUtility.Sigmoid(input);
+
+        Assert.AreEqual(expected, actual, 1e-6f);
+    }
+
+    [TestMethod]
+    public void Sigmoid_ReturnsZeroPointFiveWhenInputIsZero_UsingDouble()
+    {
+        const double input = 0f;
+        const double expected = 0.5f;
+
+        double actual = MathUtility.Sigmoid(input);
+
+        Assert.AreEqual(expected, actual, 1e-6);
+    }
+
+    [TestMethod]
+    public void Sigmoid_ReturnsZeroPointFiveWhenInputIsZero_UsingSingle()
+    {
+        const float input = 0f;
+        const float expected = 0.5f;
+
+        float actual = MathUtility.Sigmoid(input);
+
+        Assert.AreEqual(expected, actual, 1e-6f);
+    }
+
+
     [TestMethod]
     public void SmoothStep_ShouldReturnHigher_GivenAlpha1()
     {
diff --git a/X10D/src/Math/MathUtility.cs b/X10D/src/Math/MathUtility.cs
index a3549a0..dc9b2f7 100644
--- a/X10D/src/Math/MathUtility.cs
+++ b/X10D/src/Math/MathUtility.cs
@@ -333,6 +333,34 @@ public static class MathUtility
         return (alpha * newRange) + newMin;
     }
 
+    /// <summary>
+    ///     Calculates the sigmoid function for the given input value.
+    /// </summary>
+    /// <param name="value">The input value for which to calculate the sigmoid function.</param>
+    /// <returns>The result of applying the sigmoid function to the input value.</returns>
+    /// <remarks>
+    ///     The sigmoid function is a commonly used activation function in artificial neural networks and logistic regression. It
+    ///     maps any real-valued number to a value between 0 and 1.
+    /// </remarks>
+    public static float Sigmoid(float value)
+    {
+        return 1.0f / (1.0f + MathF.Exp(-value));
+    }
+
+    /// <summary>
+    ///     Calculates the sigmoid function for the given input value.
+    /// </summary>
+    /// <param name="value">The input value for which to calculate the sigmoid function.</param>
+    /// <returns>The result of applying the sigmoid function to the input value.</returns>
+    /// <remarks>
+    ///     The sigmoid function is a commonly used activation function in artificial neural networks and logistic regression. It
+    ///     maps any real-valued number to a value between 0 and 1.
+    /// </remarks>
+    public static double Sigmoid(double value)
+    {
+        return 1.0f / (1.0f + System.Math.Exp(-value));
+    }
+
     /// <summary>
     ///     Performs smooth Hermite interpolation from one value to a target using a specified alpha.
     /// </summary>