We aim to improve the prediction of residential real estate values through the use of sophisticated machine learning algorithms, specifically focusing on achieving high accuracy in forecasting house prices. Our methodology involves a methodical approach that includes data pretreatment, feature engineering, model selection, and hyperparameter tuning. Data preparation entails cautious management of missing values and elimination of superfluous characteristics to guarantee the integrity of the dataset. Subsequently, we partitioned the data into distinct training and testing sets to assess the performance of the model. Our model selection method entails evaluating a variety of machine learning techniques, such as Neural Networks (Deep Learning), CatBoost a Gradient Boosting Algorithm, and ElasticNet. Each algorithm is selected based on its distinct advantages in terms of predicted accuracy and interpretability. Throughout the conditioning of the model and the optimization of hyperparameters, we thoroughly assess the performance of the model using important metrics. The importance of our research rests in its ability to transform residential real estate forecasting, providing stakeholders with useful knowledge about house price patterns and enabling well-informed decision-making in the housing market. The aim of our study is to enhance the field of statistical analysis in real estate, promoting sustainable development and innovation within the industry. Our research demonstrates the effectiveness of incorporating machine learning approaches into projecting house values with precision. This opens up opportunities for better decision-making and expanded market insights. Our future efforts will concentrate on improving the design of models, increasing the size of datasets, and optimizing the user interface to make it more accessible to a wider range of stakeholders in the real estate industry.