Artificial Intelligence (AI) has gained prominence in recent years, being widely applied in both academic and industrial contexts. Its popularization has raised several challenges, particularly the need to make AI models auditable. Explainable Artificial Intelligence (XAI) seeks to address this issue through methods that interpret the decisions of black-box models. Despite its progress, few studies integrate XAI into the software engineering cycle. At the same time, the European Union’s AI Act (Regulation 2024/1689) requires extensive documentation for high-risk systems, often resulting in hundreds of pages of reports. To bridge this gap, this work proposes An Initial UML Approach for EU AI Act Compliance, which unifies UML, XAI, and regulatory documentation practices. The approach introduces stereotypes, tagged values, and relationships for LIME, SHAP, ICE, and Ceteris-based explanations. By graphically representing critical XAI elements, it enhances traceability and auditability while providing partial coverage of the compliance requirements, serving as a structured complement to the mandatory textual documentation. The proposal is illustrated through a case study involving a breast cancer diagnosis system.