As individuals are always changing, online social platforms can quickly create new communities and information cascades. Many apps depend on finding these occurrences early. Some of these are predicting trends, analyzing public opinion, targeted marketing, and stopping the spread of false information. Still, current approaches often assume graphs are static or employ coarse temporal aggregation, making it harder for them to capture community-aware interactions and fine-grained temporal diffusion dynamics. Because of these problems, the authors propose a mechanism for identifying new communities and viral cascades on social media platforms, called the Community Aware Temporal Pattern Diffusion Network (CTPDN). The proposed CTPDN uses both community-aware representation learning and temporal graph modeling. A community-aware attention mechanism tracks how relationships within and between communities change over time, while temporal diffusion layers operate like the spread of time-sensitive information. Using a hierarchical temporal encoder, we can observe both short-term diffusion patterns and long-term structural change. The model is trained from scratch to predict community formation and the onset of cascade virality in the early stages of diffusion. Compared with the best temporal graph and cascade prediction models, CTPDN performs far better in terms of lead-time gain, F1-score, and early-detection accuracy, as demonstrated by experimental tests on benchmark social network datasets. The results show that including explicit community awareness and temporal dispersion patterns strengthens and simplifies predictions of future social dynamics. CTPDN does substantially better than both old-school diffusion models and newer deep learning baselines, according to results from large-scale research on real-world social network datasets. The framework is effective for recognizing early viral trends, as shown by the results, which indicate an average 20–27% increase in the accuracy of early cascade detection, a 17–24% increase in the F1-score for identifying new communities, and a decrease in detection delay of up to 22%. Lastly, CTPDN increases overall performance on crucial assessment criteria by around 25% and gives a way to find early communities and cascades that can grow. CTPDN provides a scalable, efficient framework for analyzing and predicting viral cascades and the dynamic formation of communities. The proposed method not only facilitates future research in community-centric temporal graph learning but also has practical implications in real-time social analytics.