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Machine Learning is a subset and technology developed in the field of Artificial Intelligence (AI). One of the most widely used machine learning algorithms is the K-Nearest Neighbors (KNN) approach because it is a supervised learning algorithm. This paper applied the K-Nearest Neighbors (KNN) algorithm to predict the construction price index based on Vietnam's socio-economic variables. The data to build the prediction model was from the period 2016 to 2019 based on seven socio-economic variables that impact the construction price index (i.e., industrial production, construction investment capital, Vietnam's stock price index, consumer price index, foreign exchange rate, total exports, and imports). The research results showed that the construction price index prediction model based on the K-Nearest Neighbors (KNN) regression method has fewer errors than the traditional method. [ABSTRACT FROM AUTHOR]