Purpose: The proliferation of social media has fundamentally altered the public relations (PR) landscape, demanding a shift from reactive damage control to proactive, data-driven strategy. This paper addresses the growing need for a systematic approach to leveraging Artificial Intelligence (AI) for predictive social media analysis within the PR domain. It aims to bridge the gap between theoretical AI capabilities and practical PR application by proposing a comprehensive framework for identifying, analyzing, and acting upon emerging digital trends.

Methods: A systematic literature review was conducted to synthesize current research on AI applications in social media analytics—including sentiment analysis, topic modeling, and popularity prediction—and the integration of AI into PR workflows. Drawing from 12 seminal academic and industry sources, this review forms the foundation for the development of a novel conceptual framework.

Results: The research culminates in the proposal of the Predictive AI Framework for PR (PAFP). This four-phase framework outlines a structured process for (1) Data Aggregation & Filtering, (2) Trend Identification & Analysis, (3) Trajectory & Impact Prediction, and (4) Strategic PR Application. The framework integrates advanced AI techniques, such as Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNNs), into a cohesive workflow designed to provide actionable, forward-looking intelligence for communication professionals. A hypothetical case study is presented to illustrate its practical utility.

Conclusion: The PAFP provides a vital strategic tool, enabling PR professionals to move beyond mere monitoring to predictive intelligence. By harnessing AI, practitioners can anticipate public discourse, mitigate crises before they escalate, and craft more resonant and timely campaigns. This paper argues that the adoption of such frameworks is not merely an opportunity but a necessity for the future relevance and efficacy of the public relations profession.