This study employs a novel, multidisciplinary approach to explore the complex relationship between online social networking (OSN) usage patterns and mental health indicators. Using a comprehensive 2022 dataset, it combines social network analytics, behavioral data, and psychological frameworks. The analysis covers key mental health aspects such as depression, anxiety, and self-worth, while considering demographic and behavioral variables. A key innovation is the use of network visualization and analytics to identify influential nodes, critical links, and clusters that shape mental health awareness within OSNs. By leveraging centrality metrics and advanced network analysis, the study provides deep insights into the psychological impact of online interactions. The unique statistical modeling approach examines correlations between OSN interaction patterns and mental health indicators, accounting for confounding factors. Age-specific trends in social media use and their effects on mental health are identified, offering valuable information for targeted interventions. This research enhances understanding of the connection between online behavior and mental well-being, and its findings aim to inform evidence-based policies and interventions. The comprehensive methodology addresses gaps in current knowledge and offers important insights for mental health professionals, policymakers, and social media platforms in promoting healthier online interactions.