The rising demand for prediction has made it more popular and a helpful tool. Thus most of us go for prediction most of the time. Housing prices keep changing day in and day out and sometimes are hyped rather than being based on valuation. Predicting housing prices with real factors is the main crux of our research project. Here we aim to make our evaluations based on every basic parameter that is considered while determining the price. We use various machine learning techniques in this pathway, and our results are not sole determination of one technique rather it is the weighted mean of various techniques to give most accurate results. The results proved that this approach yields minimum error and maximum accuracy than individual algorithms applied. The environment and surroundings are also important features to take in mind to predict its value. But most of us will only take the building and its physical materials for consideration but the real up and down of the price values depend on those extra surrounding environments which are placed nearby. The main role of the idea is not only to predict the price but also to find some of the possibilities after the prediction has been done successfully.