Adolescent obesity is one of the most important current public health concerns, owing to its increased prevalence and adverse effects on physical and mental health. Body mass index (BMI) is a measure of obesity, and relationships between brain and BMI have been found based on univariate association analyses. However, whether/how neuroanatomical features can be used to predict the BMI and its development at the individual level during adolescence are unclear. Here, we analyzed the large-scale longitudinal IMAGEN dataset, in which structural magnetic resonance imaging and BMI were acquired at both 14 and 19 years old in the same subjects. Using the voxel-wise gray matter volume (GMV) as features and the multivariate machine learning method, we constructed predictive models for individually predicting the BMI at both 14 and 19 years old, as well as the longitudinal development of BMI between the 2 ages. We found that, the whole-brain GMV could predict the individual BMI at both 14 and 19 years old, and the development of GMV in cerebellum could predict the individual development of BMI. The contributing brain regions for predicting 14- and 19-year-old BMIs did not differ at a coarse scale, but exhibited considerable differences at a fine scale. Our results highlight the importance of GMV in predicting the individual cross-sectional BMI and its longitudinal development during adolescence.