Introduction: Body image is a core psychological construct with major implications for mental health, self-regulation, and health-related behaviours. Negative body investment is associated with eating disorders, depression, psychological distress, non-suicidal self-injury, and suicide risk, underscoring the need for clinically informative assessment. Although the Body Investment Scale (BIS) is widely used, further work on item-level dynamics and gender-related patterns may benefit from alternative analytic approaches. Materials and Methods: This cross-sectional study included 872 university students (73.7% female; mean age = 20.62 years, SD = 2.15). Item-level gender differences were examined using descriptive statistics and independent-sample t-tests. Gaussian Graphical Models (GGMs) with LASSO regularisation were estimated for the full sample and separately by gender. Network density, edge weights, centrality indices, and node predictability were evaluated; network invariance and global strength were tested. A Bayesian Network (BN) was also estimated to explore potential directional dependencies among items. Results: BIS items showed acceptable distributional properties. Several items differed significantly by gender, with small-to-moderate effects. The network was dense (86.7% of possible edges), indicating strong inter-item connectivity. Network structure was invariant across genders (p = 0.573), but global strength was higher in females (S = 2.732) than in males (S = 2.629; p = 0.043), suggesting greater overall interconnectedness of body image attitudes among women. Across models, Item 3 (“I hate my body”) showed the highest predictability (GGM R² up to 0.742; BN R² = 0.727), whereas Item 6 (“I like my appearance in spite of its imperfections”) showed the lowest predictability. Discussion: These findings support a network conceptualisation of body investment in which certain BIS items appear particularly influential. Clinically, highly predictable and strongly connected items may be useful targets for assessment, monitoring, and intervention planning, while gender differences in overall connectivity may inform tailoring of prevention and psychoeducation.