In the digital age, data-driven applications depend on much of secure, skilled and scalable data processing pipelines, especially for cases of new use in token data management, Extract-Transform-Load (ETL) processes and machine learning (ML). The study proposes a blockchain acquired data processing pipeline that distributes laser technology (DLT) for token data storage, which offers data integrity, traceability and access control. By entering data in the Blockchain framework, the proposed architecture supports decentralized data ownership that ensures secure storage and controlled data. The pipeline integrates ETL procedures to prepare data for machine learning applications, which enable streamlined data change and quality growth for ML functions. In addition, our approach provides a spontaneous transition to the Model dataset via a blockchain-based track and irreversible account book from raw data collection and ensures data fishing for data in the pipeline. The evaluation shows significant improvements in data security, interoperability and scalability, and adds minimal overhead to ETL and ML processes with blockchain. Experimental results confirm that integration increases the accuracy and reliability of the ML model by guaranteeing data integrity and audits throughout the data's life cycle. The proposed structure provides a promising basis for future decentralized and token data ecosystems with extensive applications in finance, health care and IoT.