Poverty is a problem faced by all countries, including developed and developing countries. However, this problem tends to occur more frequently in developing countries because development conditions are not yet stable. The government has made various efforts to overcome poverty through policies, such as the Village Fund program. This program is used to support government administration, development, community empowerment, and reduce poverty levels and disaster risk. Poverty is a fundamental social issue that requires serious attention from the government. Based on data from the Central Statistics Agency (BPS), there are high levels of poverty in several districts/cities, including Bogor Regency which has the highest number of poor people at the national level. This research aims to assess the influence of village funds allocated for infrastructure, empowerment, the level of regional economic growth, and the number of independent villages on poverty levels. This research method combines a qualitative descriptive approach and multiple regression analysis, using secondary data from Bogor Regency. This research uses a mixed descriptive approach which includes qualitative and quantitative aspects. This research uses multiple regression analysis using secondary data from Bogor Regency. The data analyzed includes Village Fund allocations for infrastructure development, empowerment efforts, regional economic development, and the number of independent villages during the period 2015 to 2022. The dependent variable in this research is the poverty level. The research results show that the variable village funds for infrastructure has a significant positive influence in reducing poverty levels, as does the variable number of independent villages. In contrast, the village fund variables for empowerment and the level of economic growth do not have a significant effect on the poverty level. Overall, the variables tested in this study together do not have a significant effect on poverty levels. Most of the changes in poverty levels can be explained by the variables used in the research, however, around 17.3% of the remainder is influenced by other factors outside the model.