Pedestrian Attribute Recognition (PAR) is a fundamental task in surveillance and intelligent vision systems, aiming to identify attributes such as clothing color, gender, and accessories carried from pedestrian images. In this paper, we propose a region-aware, prompt-guided, and graph-based multi-task framework developed as a solution to the PAR 2025 Contest. Our method integrates a fully fine-tuned CLIP ViT-B/32 vision-language encoder with a Graph Attention Network (GAT)-based classifier that models inter-attribute dependencies through attention-driven message passing. The system extracts visual features from both full-body and lower-body views, computes similarity scores with a curated set of 160 handcrafted textual prompts, and feeds these semantically aligned representations into a graph-based classifier. Evaluated on the private test set, our framework achieves a mean accuracy of 69.8%, demonstrating strong generalization and robustness under real-world conditions.