The real estate industry generates vast amounts of data from property listings, transactions, customer interactions, and market activities. Leveraging data science enables researchers and practitioners to uncover hidden patterns, predict trends, and gain deeper insights into customer behavior. This study aims to analyze real estate market trends and customer preferences by applying machine learning and statistical techniques to structured and unstructured datasets. Regression models, clustering algorithms, and predictive analytics are utilized to identify price determinants, customer purchasing behavior, and demand forecasting. The proposed framework not only enhances decision-making for investors, developers, and agents but also contributes to improving personalized recommendations for customers. The study demonstrates how data-driven insights can optimize property valuation, marketing strategies, and overall customer satisfaction in the real estate sector.