This chapter examines how network analysis, an innovative statistical approach in psychological and behavioral health sciences, can be applied to international research. Unlike traditional network methods from sociology, the network theory in health sciences represents a novel framework conceptualizing mental disorders as systems of interacting symptoms rather than manifestations of latent conditions. This chapter introduces network analysis as a valuable statistical framework for researchers interested in applying this approach to cross-disciplinary health assessments. It highlights the method’s potential for uncovering complex interrelationships among variables in health research. The chapter discusses methodological considerations, including the use of partial correlations to estimate links without assuming causality, and compares frequentist methods such as the Least Absolute Shrinkage and Selection Operator (LASSO) regularization with Bayesian estimation approaches. The chapter further demonstrates how network analysis and its associated parameters, like centrality and predictability metrics, identify key intervention targets across diverse cultural contexts, revealing both culture-specific patterns and cross-cultural psychological mechanisms. We address methodological challenges in implementing these techniques and provide practical guidelines for researchers. This chapter serves as both an introduction to psychological network analysis and a roadmap for applying these methods to advance the understanding of cross-cultural health research.