<p>Probabilistic dual hesitant fuzzy sets effectively characterize hesitant fuzzy information alongside its associated probability distribution, serving as a robust tool for managing uncertainty. Addressing the research void concerning group decision-making with probabilistic dual hesitant fuzzy sets within social network contexts, this paper proposes a group decision-making method grounded in probabilistic dual hesitant fuzzy preference relations. The concept and assessment approach for geometric consistency are introduced, and an optimization model is subsequently formulated to enhance consistency levels. Furthermore, a consensus-reaching framework leveraging dynamic social network trust is developed. This framework guides experts toward consensus by supplementing trust relationships, computing expert trust weights, conducting multi-level consensus evaluations, and iteratively adjusting both trust relationships and preferences. Additionally, a methodology for dynamically determining experts’ comprehensive weights is proposed, integrating social network trust with group consensus levels and employing the BrowseRank algorithm. Finally, the effectiveness and practicality of the proposed method are validated through numerical case studies.</p>