Over the past decade, the popularity and adoption of social media have increased globally, making it an inevitable part of daily communication and connection. However, the widespread use of social platforms introduces significant challenges to mental well-being. Ongoing state-of-the-art research demonstrates a robust correlation between extensive social media usage and the worsening of medical conditions such as depression and anxiety. These approaches often struggle because they fail to adequately model key health-related information and screen usage frequency in their analyses. Moreover, most of the existing methods do not fully leverage data analytics and machine learning technologies to effectively model the influence of social media algorithms on different age groups. In this paper, we employ a multi-model analysis, utilizing techniques such as decision trees, random forests, support vector machines (SVMs), and convolutional neural networks (CNNs), to gain a deeper understanding of the intricate relationship between social media use and mental health. In particular, we present four case studies exploring demographic risk factors, broader health consequences and specific health behaviors, and depression predictors associated with the use of social media. These analyses provide a comprehensive view of social media’s multi-dimensional impact on mental health and offer critical understandings for designing targeted interventions and policies.