Visible-Infrared Person Re-Identification (VI-ReID) remains highly challenging because of the substantial discrepancy between visible and infrared modalities. Although recent approaches incorporate attribute learning to improve cross-modal consistency, they often rely on complex architectures that separately process identity and attribute features, which increases model complexity and restricts scalability. To overcome these limitations, we propose a Unified Identity and Attribute Learning (UIAL) framework, which unifies identity and attribute feature extraction within a streamlined structure. UIAL introduces an Attribute-Enhanced Recognition Module (AERM) to jointly capture identity and attribute features and employs a Cross-Modal Coherence Loss (CMCL) for identity-level clustering and separation, alongside consistent intra-identity cross-modal clustering, enhancing feature consistency across modalities and enabling a more robust unified model. We evaluate UIAL through extensive experiments, showing that it establishes a strong foundation for future attribute-integrated VI-ReID research, achieving state-of-the-art performance.