The Mission-critical IIoT (MC-IIoT) networks are highly interconnected and distributed to ensure real-time monitoring and control, where security, reliability, and availability are paramount. These features make MC-IIoT networks vulnerable to Distributed Denial of Service (DDoS) attacks that must be mitigated while combating single-point of failure of attack detection systems, limited scalability of industrial control systems, high latency and low reliability. In this paper, we propose a Blockchain-based DDoS attack mitigation framework for MC-IIoT networks which persuades these requirements. Initially, a machine learning-based cascaded model consisting of an autoencoder-enhanced decision tree model and a lightweight isolation forest algorithm performs attack detection and trust score evaluation of IIoT devices. A consortium Blockchain network of IIoT gateways on which this cascaded model is installed performs attack mitigation using a chaincode based on previously evaluated trust scores. The proposed model is evaluated on Hyperledger Fabric and the performance is analyzed with Hyperledger caliper benchmarking. The comparative analysis of the proposed framework with state-of-the-art proves its effectiveness in terms of attack detection accuracy (99.93%), CPU utilization (30%), decreased latency (12.5 ms), increased throughput (21%), chaincode execution time (3 ms) and attack detection rate (99.6%).