Word2Vec is one of the earlier and most popular examples of word embedding approach. It was created training a neural network architecture that predicts the context of a word given the target word or vice versa.
The main idea behind this technique is that words that appear in similar contexts tend to have similar meanings, and consequently, when projected into two-dimensional word embeddings for visualization, simple terms are displayed as clustered together.
The Word2Vec model is used as a pretrained model to generate embeddings for Machine Learning models.