The real estate market is a cornerstone of global economics and social dynamics, significantly shaping housing decisions and investment strategies. Within this context, the accurate prediction of housing prices is essential. Leveraging advanced Machine Learning (ML) techniques, this research delves into the pivotal role of the real estate market, highlighting its economic and social importance, particularly regarding housing prices. The study aims to develop a robust property price prediction model by considering crucial factors such as location and size. Recognizing the challenges posed by the dynamic nature of the real estate market, the research endeavors to create an accurate and reliable forecasting tool with a user-friendly interface. With urbanization driving migration trends, traditional methods reliant on real estate brokers often prove inadequate, necessitating accessible interfaces empowering users to obtain precise property price predictions independently. Ultimately, this research aims to bridge the information gap within the real estate sector by providing a data-driven solution in the form of a machine learning model. The study's focus encompasses Support Vector Machines (SVMs), Random Forest, and Gradient Boosting models. Notably, the Random Forest model demonstrates promising results, yielding an R-squared value of 0.8 and a low error rate of 0.28, indicating significant improvement over previous methodologies. The implications of this study extend to consumers, industry professionals, and policymakers alike, fostering enhanced efficiency and equity within housing markets. Looking ahead, ongoing research in ML-driven predictive modeling holds the potential to revolutionize real estate decision-making processes, ensuring adaptability to evolving market dynamics and bolstering overall market efficiency.