Market trends are increasingly becoming significant in this world as the society grows rapidly and their demands shift towards markets. In order to make intelligent decisions, it is necessary to be in a position to give precise estimations of the properties considering the prevailing trends. It assists individuals to organize their financial needs in a manner that is intact with their visions. The real estate business plays a major role in this as it is ever expanding. Market trend is something that is of importance to the investor who wishes to make a judicious investment that will benefit him/her in the best manner possible. However, customers experience difficulty in the case of real estate pricing because it is not transparent and middlemen normally overcharge. Due to availability of large sets of data, researchers can now create more accurate models of prediction. The traditional models usually suffer such problems as being not as accurate and also overfitting and less of use. These issues are addressed in the proposed system, however, which offers a powerful and effective model along with a friendly user interface. The overall point of the work is to develop a solution applicable to all participants, customers and firms, which will reduce manual labor and save time and money. Among machine learning techniques applied in this system are Lasso Regression, Linear Regression, and even Decision Tree. Combination of these algorithms using the stacking method aims to enhance their accuracy and performance. The aim of the proposed approach is to offer the tool that is user-friendly and reliable, simplifies the decisions in real estates and forecasts accurately.