The proposed research focus on accomplish better Modelling methods and Classification technique to integrate urban growth and utility management with the help of Spatio-Temporal Data Mining. Now a days the urban growth increasing rapidly in other hand providing utility services to the society getting congestion and hassle. This research aid to classify urban growth level using Spatial-Temporal data and better utility service system is attain with the facilitate of Knowledge Based Integration (KBI). The broader concept of urban growth modelling provide the detail of Land cover/ Land use ,Changes, Growth or Reduction of feature in area extract with the assist of Multiple Level Classification (MLC). In the province of urban growth modelling can be make use of scrutinize, estimate and forecasting urban systems to support Utility Management and Decision-Making process. In Remote sensing technique, modelling attains from the Spatial and Temporal elements obtain from Topographic Maps, Aerial Photos, Satellite Images, Several Databases and Statistical Information from Private or Government Organization. The proposed “Multiple Level Classification” (MLC) technique consists of Cellular Automata (CA) and Spatial statistics, etc. Thus the hierarchy level of urban growth classification have to integrate among Utility Management with the facilitate of “Knowledge Based Integration” (KBI) includes techniques such as Artificial Neural Network (ANN) and Fuzzy Logic. In this utility covers the basic service such Electricity Transmission elements, Water supply system, Hospital/Emergency unit, Fuel /Gas link , Road Network and Telecommunication Network, etc. This research mainly helps to provide various utility services in efficient way to the developing urban and also diminish the time span and cost for utility service.