Random Forest, an ensemble learning technique, builds multiple decision trees during training and combines their outputs for robust predictions. Each tree is constructed with a random subset of the training data and a random subset of features, reducing overfitting. The final result is determined by aggregating the predictions of individual trees, providing improved accuracy and generalization compared to a single tree. Random Forests are versatile, handling classification and regression tasks, and they excel in diverse domains like finance, healthcare, and image analysis. Their ability to mitigate overfitting and enhance predictive power makes them a widely used and effective machine learning approach.