Social media platforms have become critical sources of real-time information for monitoring and predicting civil unrest and violent events. Recent advances in artificial intelligence have produced a wide range of analytical pipelines, including transformer-based language models, graph neural networks, temporal forecasting systems, and multimodal vision–language frameworks. However, existing studies remain fragmented across platforms, languages, and modeling paradigms, making it difficult to assess their relative effectiveness and applicability. This paper presents a comprehensive comparative review of AI-based architectures used for civil unrest prediction using Twitter and Instagram data. The study systematically analyzes model categories, data representations, performance trends, platform suitability, multilingual capability, and ethical considerations. By synthesizing findings across recent literature, this work highlights architectural trade-offs, identifies persistent research gaps, and provides practical guidance for selecting appropriate analytical frameworks. The review contributes toward a clearer understanding of current methodological capabilities and limitations in socially responsible unrest prediction.