Machine learning methods and techniques are a current reality in artificial intelligence, which is rapidly developing and encompassing most aspects of social life. The \(k\) -nearest neighbors ( \(k\) -NN) algorithm is a prevalent machine learning method for classification and regression problems. It is based on the assumption that similar data points will have similar labels or values. \(k\) -NN is a non-parametric algorithm, meaning it makes no assumptions about the input’s probability distribution. In this paper, we developed a model that uses the \(k\) -NN algorithm to study the USA real estate market based on house price classification. When looking to buy a property, one of the most important factors to consider is the price. Therefore, we trained a model to classify new data into a specific price category with its own set of characteristics, thereby facilitating buyer’s decision-making processes.