Route planners that identify the best step-by-step driving directions between two locations have rapidly become a part of our everyday life. However, the output quality of route planners depends critically the current status of roads in order to calculate the best (shortest, fastest, or cheapest) route from a starting point to a destination. Maintaining accurate and updated statistics about the status of roads has been made possible due to the integration of navigational technologies. These technologies are the global positioning system (GPS), database technologies such as geographic information system (GIS) and communication technology. This paper is to propose an efficient self learning mechanism or framework to help rout planners find out best routes based on the current status of roads in addition to the static road lengths. This helps customizable best rout identification. With the proposed framework, it is possible, for instance, to choose the best route that the minimal time in addition to minimal fuel consumption, which is a very critical factor due to the significant increase in fuel prices in the last few years. An advantage of the proposed framework is that is does not require any extra streets equipments. Further, the proposed framework promises a system that requires minimal administration.