A correct prediction of a contractor’s work can lead to many important things like keeping corruption in check, determining whether that particular is fit for the job or not. Frequently, it is brought that prediction is chaotic rather than random, which means it can be predicted by carefully analysing the history of respective contractor. Machine learning is an efficient way to represent such processes. It predicts a value close to the tangible value, thereby increasing the accuracy. The vital part of machine learning is the dataset used. The dataset should be as concrete as possible because a little change in the data can perpetuate massive changes in the outcome. It consists of variables like name of contactors, previous projects that he worked on, budget of that project, estimated lifespan of the project and actual life of the project. Due to the serious problem of bad road construction and maintenance, the real-time road situation and the possibility of road conditions in the next time period should be taken into accounts through the vehicle navigation system, in order to provide the optimal routing plans for vehicles in routing optimization. To solve the ignoring of real-time travel information and historical travel information in the existing navigation systems during routing optimization, this paper compares the existing navigation system to a prototype system which considers the conditions of the roadways as one of the factor in the recommendation and optimization system of the path finding and navigation process.