This research investigates the synergies between Decentralized Ledger Technology (DLT) and Machine Learning (ML) algorithms to enhance the security and traceability of visual data. In an era of increasing reliance on visual information, ensuring the integrity and origin of such data is crucial for applications ranging from digital forensics to multimedia content verification. Decentralized ledger technologies like blockchain offer a decentralized and tamper-resistant framework for maintaining transparent and immutable records. The study explores how DLT can be integrated with ML algorithms to create a robust system for validating visual data authenticity. ML algorithms are employed for real-time analysis of visual content, detecting anomalies and inconsistencies that may indicate tampering or manipulation. These algorithms contribute to an intelligent layer that complements the decentralized nature of DLT, reinforcing the trustworthiness of visual data.