The use of artificial intelligence in government and its implications to society have generated widespread interest from research and practice communities. However, the analysis of artificial intelligence techniques as a tool to support government decision-making in specific functions is still scarce. For instance, public budgeting could be considered one of the most important internal functions of government and, therefore, it is necessary to understand how artificial intelligence could impact this function. This paper explores the potential of artificial intelligence techniques to classify public budgeting allocations to different programs and policies. In order to achieve this, artificial neural networks and genetic algorithms are applied to budget data from the Mexican federal government. Among other specific actions, the results suggest that government decision makers should consider fostering investments in social and economic development, as well as increasing the budget in non-programmable expenses in order to activate the economy. Based on the results of this study, we argue that the use of artificial intelligence techniques in government, as a tool for data analysis, has the potential to support better decision-making in government. In our view, artificial intelligence should not be necessarily used to make the final decisions automatically, but to provide ideas and identify potential opportunities and different scenarios that government leaders can then assess and incorporate into their decision-making processes.