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The real estate industry is witnessing a significant evolution through the application of Big Data Analytics, which allows for more precise, data-driven forecasting of market trends. This study investigates how large-scale and diverse data sources—such as property transaction records, demographic statistics, economic indicators, geospatial data, and social media sentiment—can be effectively utilized to predict market behavior. By incorporating machine learning algorithms, statistical modeling techniques, and real-time data analysis, the research demonstrates how predictive analytics can provide meaningful insights for stakeholders like investors, developers, and urban policymakers. The paper outlines a complete data pipeline including data collection, preprocessing, model training, and evaluation using methods like regression, decision trees, and neural networks. Key applications explored include forecasting price trends, analyzing demand and supply, and estimating neighborhood values. Challenges such as data integration, privacy concerns, and the interpretability of complex models are also addressed. The findings emphasize the potential of Big Data to enhance investment decision- making, reduce market uncertainties, and support more informed urban planning strategies