diff --git a/Algorithms.Tests/MachineLearning/LinearRegressionTests.cs b/Algorithms.Tests/MachineLearning/LinearRegressionTests.cs
new file mode 100644
index 00000000..c43fc41b
--- /dev/null
+++ b/Algorithms.Tests/MachineLearning/LinearRegressionTests.cs
@@ -0,0 +1,85 @@
+using Algorithms.MachineLearning;
+
+namespace Algorithms.Tests.MachineLearning;
+
+/// <summary>
+/// Unit tests for the LinearRegression class.
+/// </summary>
+public class LinearRegressionTests
+{
+    [Test]
+    public void Fit_ThrowsException_WhenInputIsNull()
+    {
+        var lr = new LinearRegression();
+        Assert.Throws<ArgumentException>(() => lr.Fit(null!, new List<double> { 1 }));
+        Assert.Throws<ArgumentException>(() => lr.Fit(new List<double> { 1 }, null!));
+    }
+
+    [Test]
+    public void Fit_ThrowsException_WhenInputIsEmpty()
+    {
+        var lr = new LinearRegression();
+        Assert.Throws<ArgumentException>(() => lr.Fit(new List<double>(), new List<double>()));
+    }
+
+    [Test]
+    public void Fit_ThrowsException_WhenInputLengthsDiffer()
+    {
+        var lr = new LinearRegression();
+        Assert.Throws<ArgumentException>(() => lr.Fit(new List<double> { 1 }, new List<double> { 2, 3 }));
+    }
+
+    [Test]
+    public void Fit_ThrowsException_WhenXVarianceIsZero()
+    {
+        var lr = new LinearRegression();
+        Assert.Throws<ArgumentException>(() => lr.Fit(new List<double> { 1, 1, 1 }, new List<double> { 2, 3, 4 }));
+    }
+
+    [Test]
+    public void Predict_ThrowsException_IfNotFitted()
+    {
+        var lr = new LinearRegression();
+        Assert.Throws<InvalidOperationException>(() => lr.Predict(1.0));
+        Assert.Throws<InvalidOperationException>(() => lr.Predict(new List<double> { 1.0 }));
+    }
+
+    [Test]
+    public void FitAndPredict_WorksForSimpleData()
+    {
+        // y = 2x + 1
+        var x = new List<double> { 1, 2, 3, 4 };
+        var y = new List<double> { 3, 5, 7, 9 };
+        var lr = new LinearRegression();
+        lr.Fit(x, y);
+        Assert.That(lr.IsFitted, Is.True);
+        Assert.That(lr.Intercept, Is.EqualTo(1.0).Within(1e-6));
+        Assert.That(lr.Slope, Is.EqualTo(2.0).Within(1e-6));
+        Assert.That(lr.Predict(5), Is.EqualTo(11.0).Within(1e-6));
+    }
+
+    [Test]
+    public void FitAndPredict_WorksForNegativeSlope()
+    {
+        // y = -3x + 4
+        var x = new List<double> { 0, 1, 2 };
+        var y = new List<double> { 4, 1, -2 };
+        var lr = new LinearRegression();
+        lr.Fit(x, y);
+        Assert.That(lr.Intercept, Is.EqualTo(4.0).Within(1e-6));
+        Assert.That(lr.Slope, Is.EqualTo(-3.0).Within(1e-6));
+        Assert.That(lr.Predict(3), Is.EqualTo(-5.0).Within(1e-6));
+    }
+
+    [Test]
+    public void Predict_List_WorksCorrectly()
+    {
+        var x = new List<double> { 1, 2, 3 };
+        var y = new List<double> { 2, 4, 6 };
+        var lr = new LinearRegression();
+        lr.Fit(x, y); // y = 2x
+        var predictions = lr.Predict(new List<double> { 4, 5 });
+        Assert.That(predictions[0], Is.EqualTo(8.0).Within(1e-6));
+        Assert.That(predictions[1], Is.EqualTo(10.0).Within(1e-6));
+    }
+}
diff --git a/Algorithms/MachineLearning/LinearRegression.cs b/Algorithms/MachineLearning/LinearRegression.cs
new file mode 100644
index 00000000..4e586376
--- /dev/null
+++ b/Algorithms/MachineLearning/LinearRegression.cs
@@ -0,0 +1,100 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+namespace Algorithms.MachineLearning;
+
+/// <summary>
+/// Implements simple linear regression for one independent variable (univariate).
+/// Linear regression is a supervised learning algorithm used to model the relationship
+/// between a scalar dependent variable (Y) and an independent variable (X).
+/// The model fits a line: Y = a + bX, where 'a' is the intercept and 'b' is the slope.
+/// </summary>
+public class LinearRegression
+{
+    // Intercept (a) and slope (b) of the fitted line
+    public double Intercept { get; private set; }
+
+    public double Slope { get; private set; }
+
+    public bool IsFitted { get; private set; }
+
+    /// <summary>
+    /// Fits the linear regression model to the provided data.
+    /// </summary>
+    /// <param name="x">List of independent variable values.</param>
+    /// <param name="y">List of dependent variable values.</param>
+    /// <exception cref="ArgumentException">Thrown if input lists are null, empty, or of different lengths.</exception>
+    public void Fit(IList<double> x, IList<double> y)
+    {
+        if (x == null || y == null)
+        {
+            throw new ArgumentException("Input data cannot be null.");
+        }
+
+        if (x.Count == 0 || y.Count == 0)
+        {
+            throw new ArgumentException("Input data cannot be empty.");
+        }
+
+        if (x.Count != y.Count)
+        {
+            throw new ArgumentException("Input lists must have the same length.");
+        }
+
+        // Calculate means
+        double xMean = x.Average();
+        double yMean = y.Average();
+
+        // Calculate slope (b) and intercept (a)
+        double numerator = 0.0;
+        double denominator = 0.0;
+        for (int i = 0; i < x.Count; i++)
+        {
+            numerator += (x[i] - xMean) * (y[i] - yMean);
+            denominator += (x[i] - xMean) * (x[i] - xMean);
+        }
+
+        const double epsilon = 1e-12;
+        if (Math.Abs(denominator) < epsilon)
+        {
+            throw new ArgumentException("Variance of X must not be zero.");
+        }
+
+        Slope = numerator / denominator;
+        Intercept = yMean - Slope * xMean;
+        IsFitted = true;
+    }
+
+    /// <summary>
+    /// Predicts the output value for a given input using the fitted model.
+    /// </summary>
+    /// <param name="x">Input value.</param>
+    /// <returns>Predicted output value.</returns>
+    /// <exception cref="InvalidOperationException">Thrown if the model is not fitted.</exception>
+    public double Predict(double x)
+    {
+        if (!IsFitted)
+        {
+            throw new InvalidOperationException("Model must be fitted before prediction.");
+        }
+
+        return Intercept + Slope * x;
+    }
+
+    /// <summary>
+    /// Predicts output values for a list of inputs using the fitted model.
+    /// </summary>
+    /// <param name="xValues">List of input values.</param>
+    /// <returns>List of predicted output values.</returns>
+    /// <exception cref="InvalidOperationException">Thrown if the model is not fitted.</exception>
+    public IList<double> Predict(IList<double> xValues)
+    {
+        if (!IsFitted)
+        {
+            throw new InvalidOperationException("Model must be fitted before prediction.");
+        }
+
+        return xValues.Select(Predict).ToList();
+    }
+}
diff --git a/README.md b/README.md
index cb1358d0..37165565 100644
--- a/README.md
+++ b/README.md
@@ -106,6 +106,8 @@ find more than one implementation for the same objective but using different alg
     * [SoftMax Function](./Algorithms/Numeric/SoftMax.cs)
   * [RecommenderSystem](./Algorithms/RecommenderSystem)
     * [CollaborativeFiltering](./Algorithms/RecommenderSystem/CollaborativeFiltering)
+  * [Machine Learning](./Algorithms/MachineLearning)
+    * [Linear Regression](./Algorithms/MachineLearning/LinearRegression.cs)
   * [Searches](./Algorithms/Search)
     * [A-Star](./Algorithms/Search/AStar/)
     * [Binary Search](./Algorithms/Search/BinarySearcher.cs)