Random forest is an easy-to-use and powerful ML algorithm. It is also a supervised algorithm and requires labeled data to learn from. In fact, the decision tree acts as the building block for the random forest algorithm. Just like the decision tree, the random forest ML algorithm can be used for classification as well as regression.
The fundamental motivation behind the random forest algorithm is to combine results from multiple random decision trees into a single model. One very nice outcome of the random forest algorithm is that it prevents overfitting of the model to the training dataset.