Neural Message Passing is a technique used in graph neural networks where nodes in a graph iteratively exchange and update information with their neighbors to learn representations. This method enables the model to capture complex dependencies and relational information inherent in graph-structured data, making it powerful for tasks like node classification and link prediction.