The rapid growth of cities has led to a significant shortage of affordable housing, exacerbated by unplanned development and rising population density. This project was initiated to tackle the pressing housing issues by using data-driven analysis to identify neighborhoods that offer a balanced mix of essential amenities. Our main goal is to support homebuyers by recommending neighborhoods that are well-equipped with necessary amenities, thus enabling more informed and efficient housing choices in complex urban areas. This methodology can be applied to other cities facing similar challenges related to urban expansion. For this project, we utilized the Foursquare Places API to gather data on various amenities in different neighborhoods, including hospitals, supermarkets, parks, and public transportation services. Additionally, we incorporated housing price data to provide budget options, allowing potential buyers to find neighborhoods within their financial limits. Through K-Means clustering, we grouped neighborhoods based on the availability of amenities, aiming to highlight areas with the most potential for growth and affordability. The clustering results were evaluated based on the number of clusters and the distribution of neighborhoods within them. K- Means proved effective in grouping neighborhoods with similar amenities, helping us identify areas that offer a balanced mix of amenities and affordability. The algorithm provided valuable insights by organizing neighborhoods into distinct clusters, giving homebuyers a clearer understanding of the best options available within their budget.