This paper presents an innovative optimization framework aimed at data mining in social networks, guaranteeing solutions for some of the basic challenges of computational efficiency, scalability, and accuracy. This work presents a precise approach that integrates state-of-the-art algorithmic enhancements with dynamic resource management techniques. Extensive experimental validation using real and synthetic datasets has marked the significant performance gains achieved within the framework. These results point to a 70.2% reduction in processing time and a 71.2% saving in memory consumption, all while maintaining accuracy rates above 95%. This optimization framework is very stable under different operation conditions, since its responses have always remained below 85 ms under peak loads of up to 245,000 requests per second. The empirical evaluation of the framework across diverse social networking platforms bears testimony to the fact of practical efficacy and has emerged strongly while dealing with dynamic network architecture with extensive data processing needs. The application results in significant improvement in resource utilization efficiency, providing sub-linear increase in memory consumption for maintaining consistent performance under fluctuating load scenarios. The present study extends the scope of social network analysis by proposing a scalable, efficient, and reliable optimization framework that might be of vital importance in both research and practical implementation contexts.