Social Networks Analysis (SNA) represents one of the most important aspects of modern communication between individuals, resulting in large complex datasets which are hard to analyze. In this paper, DynaSoM—Dynamic Self-Organizing Maps addressing these challenges by using dynamic graph embeddings and adaptive SOM in real-time, multi-modal social network analysis is introduced. DynaSoM’s key innovations include: Five qualities are as follows first, dynamic graph embeddings to support efficient updates, second, SOM has a self-organizing structure, third, data from different sources can be integrated, fourth, embeddings are made fair to avoid bias, and lastly, the visualizations are explanatory. Evaluated on Twitter, Facebook and LinkedIn, DynaSoM demonstrates better results in link prediction (5%–7% AUC-ROC), node classification (4%–6% F1-score), and community detection while processing the networks 2.5 times faster improvement. DynaSoM provides practical frameworks for scalable and real-time analysis for trend detection, influencer identification and emerging community tracking, and lays the foundation for new benchmark in dynamic, multi-mode, and ethical SNA.