The rapid proliferation of artificial intelligence (AI) systems has led to the generation of massive amounts of valuable data, which can be leveraged to enhance analytical capabilities and support intelligent decision-making. Since the quality of such decision-making is fundamentally determined by the AI models trained on these data, it is crucial to perform periodic audits to verify and maintain data integrity. However, traditional data auditing solutions encounter significant challenges due to the reluctance of different domains to share data. To this end, we propose a Blockchain-based Cross-Domain Data Auditing (BCDDA) scheme designed for trustworthy AI systems. BCDDA employs a Chameleon Hash-based mechanism to efficiently construct audit metadata, thereby reducing the computational overhead on domain servers. Furthermore, it adopts a parallel data auditing architecture, in which audit tags are recorded on the main blockchain, while AI data are redundantly stored across multiple domain servers to ensure recoverability even if a shard is compromised. In addition, BCDDA supports dynamic operations on AI data, such as insertion, deletion, and modification, without regenerating audit tags, thereby enhancing system flexibility. Experimental evaluations conducted with four shard consensus nodes demonstrate that the proposed BCDDA scheme achieves high efficiency in audit tag generation and shard consensus processes. The results confirm that BCDDA provides a secure, reliable, and scalable solution for cross-domain AI data auditing in decentralized environments.