Accurate house price prediction is a vital task in the industries, as it enables stakeholders to make uniform decisions and minimize risks. With the increasing trends of datasets of various outlets and computational resources, the techniques of machine learning have gained popularity in this domain. This paper proposes a deep learning approach using TensorFlow for house price prediction, leveraging a comprehensive dataset of real estate transactions. Our model incorporates the features of the model, including property attributes, location-based characteristics, and economic indicators, to predict house prices with high accuracy. We evaluate the performance of different model of the paper using various metrics, including MSE, RMSE, and R-squared, adjusted R-squared and compare it with traditional machine learning approaches. The results demonstrate that our TensorFlow-based model outperforms existing methods, achieving a significant improvement in prediction accuracy. Furthermore, we deploy our model as a web application, enabling users to input property features and receive predicted house prices in real-time. The contribution of the research of intelligent systems for house price prediction, providing a valuable tool for real estate professionals, investors, and policymakers is taken in consideration.