Investigating the development of ICT in different countries, particularly in developing nations, holds significant importance. The advent of machine learning has opened new avenues for analysis in diverse industries, including economics. In this study, machine learning methods are applied to extract factors influencing ICT development using data obtained from the World Bank. The data is processed using LASSO regression and XGBoost regression methods to predict the “Computer, communications, and other services (% of commercial service exports)” parameter and identify the most significant factors impacting the target parameter. The XGBoost model's capability to yield accurate results with only a limited number of parameters allows for substantial data reduction while retaining the economic advantages of identifying the most informative features.