Cyberviolence on social media, such as harassment, hate speech, and cyberbullying, is becoming a bigger problem because it takes advantage of cognitive weaknesses like attentional fatigue, biased information processing, and long-term exposure to harmful content. Most current solutions use reactive strategies, such as natural language processing (NLP) and multimodal deep learning, to find abusive content after it has already happened. This paper puts forward a Cognitive-Proactive Unified Framework that combines cognitive vulnerability modeling with cyberviolence detection to make it possible to make predictions and take steps to stop them. The framework consists of three main parts: (1) Cognitive Vulnerability Prediction, which finds early signs that a user is vulnerable; (2) Cyberviolence Detection, which uses lightweight NLP models to look at text and multimodal content; and (3) a Predictive–Proactive Intervention Layer, which links user vulnerabilities to cyberviolence risks to send early warnings, suggest ways to reduce risks, or redirect content. A prototype created with publicly available datasets of hate speech and cyberbullying is put to the test in a simulated case study. This shows that the framework can predict exposure risks and offer proactive protection. As far as we know, this is the first time someone has tried to put a cognitive-proactive approach to stopping cyberviolence on social media into action.