The growing demand for synthetic data in privacy-sensitive domains necessitates scheduling frameworks that balance generation efficiency with verifiable traceability. Existing approaches often struggle to maintain secure auditability while scaling across distributed environments. This paper presents TrustSched, a distributed scheduling framework integrating blockchain-backed integrity proofs for trusted synthetic data generation. By employing a Master-Executor architecture with slot-epoch scheduling, the system achieves parallelized data synthesis while enforcing hierarchical Merkle tree commitments for tamper-evident traceability. A batched blockchain submission mechanism decouples evidence anchoring from task execution, optimizing both computational throughput and on-chain efficiency. Experimental validation confirms the framework’s ability to maintain robust security guarantees without compromising synthesis performance, demonstrating its suitability for enterprise applications requiring auditable data generation.