Accurate and efficient prediction of house prices is a critical challenge in the real estate market. This research aims to develop a robust machine learning model capable of estimating property values. By systematically analyzing key determinants of house prices, including location, square footage, and number of bedrooms, this study seeks to contribute to the advancement of property valuation methodologies. Our research investigated the complex interplay between house features and their corresponding prices. The study involved the meticulous examination of a comprehensive dataset, allowing for a thorough understanding of market trends and patterns. Various machine learning algorithms were rigorously tested and compared to identify the most effective model for predicting house prices. The findings of this research demonstrated that linear regression emerges as the superior algorithm for estimating property values within the given dataset. Furthermore, the study highlights the significant influence of specific features, such as bathroom and bedroom numbers, on predicted prices. These insights underscore the importance of considering a holistic range of factors when evaluating property values. The developed model holds the potential to revolutionize the real estate industry by providing stakeholders with a reliable tool for informed decision-making. By accurately predicting house prices, this research contributes to enhancing market efficiency, optimizing investment strategies, and supporting equitable property valuations.