Search engines are exceedingly dependent on the query auto-completion. Query auto-completion is an ongoing activity that puts forwards a group of words for every click dynamically. Query suggestions help in formulating the query and improving the quality of the search. Graphs are data structures that are universal and extensively used in computer science and related fields. The graph machine learning approach is growing rapidly with applications such as friendship recommendation, social network, and information retrieval. Node2vec algorithm is used to study the feature illustration of nodes in a graph. It is derived by word embedding algorithm Word2vec. A supervised Recurrent Neural Network using Long short-term memory (LSTM) is employed to compute the accuracy. This model confirms 89% accuracy for query auto-completion. Greater the accuracy better the model.