Urban planning is crucial to sustainable growth. In order for the planners to make decisions effectively, data from multiple sources have to be retrieved and cross referenced, which is often time consuming. We discuss the implementation of a query engine which accepts natural language as input, using machine learning and NLP techniques namely word embedding, CNN, rule-based system and NER to produce accurate output enriched with geographical insights to facilitate the planning process. The query engine classifies the input query into one of the planning domains, as well as determines the category, lat-long of the location and the size of buffer if was mentioned in the query. Processed results are presented on the map services of geographical information system.