  Global concern over food prices and security has been exacerbated by the
impacts of armed conflicts such as the Russia Ukraine War, pandemic diseases,
and climate change. Traditionally, analyzing global food prices and their
associations with socioeconomic factors has relied on static linear regression
models. However, the complexity of socioeconomic factors and their implications
extend beyond simple linear relationships. By incorporating determinants,
critical characteristics identification, and comparative model analysis, this
study aimed to identify the critical socioeconomic characteristics and
multidimensional relationships associated with the underlying factors of food
prices and security. Machine learning tools were used to uncover the
socioeconomic factors influencing global food prices from 2000 to 2022. A total
of 105 key variables from the World Development Indicators and the Food and
Agriculture Organization of the United Nations were selected. Machine learning
identified four key dimensions of food price security: economic and population
metrics, military spending, health spending, and environmental factors. The top
30 determinants were selected for feature extraction using data mining. The
efficiency of the support vector regression model allowed for precise
prediction making and correlation analysis.
  Keywords: environment and growth, global economics, price fluctuation,
support vector regression
