Real estate is known as one of the most important sectors of the economy, hence Machine Learning Techniques outperforms a real estate professional when it comes to conducting demographic market research, environmental and financial analysis such as price predictions and loan approvals. The main objective of this paper is to find the best model to predict the prices and loan approvals and apply them to develop a smart E-Commerce application where users can search and inquire about a property, post their property Ads and can use the smart feature to predict the eligibility for their loan approvals using Machine Learning Model. The first thing that we have taken into account is the dataset of the apartment prices and different representative cases of loan applications. The dataset was pre-processed and tuned up for real analysis. Hence, our paper will also focus on data preprocessing of the raw dataset. Secondly after processing the data, we will review the use of Neural Network and Random Forest and the outcome that it generates. In addition, the proposed paper examines the use of the prediction system in real world settings applied into the smart real estate web application. The successful predictions made by the models will be a great asset for the real estate sectors and will provide real-life solutions to the problems that real estate investors face.