Word embeddings are numerical vector representations of words that capture semantic relationships and contextual meanings, enabling machines to understand and process natural language effectively. They transform words into multidimensional space where similar words are positioned closer together, facilitating tasks like sentiment analysis, translation, and information retrieval.