A random forest is a collection of many decision trees whose predictions get combined into one. Each tree sees a different random sample of rows and a different random subset of features at each split. For classification the trees vote; for regression their outputs are averaged.
The difference from one tree is stability. A single deep tree fits the noise in its training data, and swapping a few rows can change its whole structure. Averaging many trees that each err in different directions cancels most of that noise. The forest usually beats the single tree on unseen data by a wide margin.
You pay for that in two ways. Hundreds of trees cost more memory and more prediction time than one. You also lose the readable if-then path and get ranked feature importances instead. Training parallelises easily, since the trees never depend on each other.
Rewriting in plainer words…
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