Abstract <p>Blockchain is a secure, decentralized ledger system that records transactions in immutable blocks. A smart contract is a self-executing piece of code on the blockchain that automatically enforces agreements when specific conditions are met. Additionally, once deployed, smart contracts are immutable, making it difficult to fix bugs or vulnerabilities without affecting the entire blockchain. Using machine learning and deep learning techniques, vulnerabilities in smart contract code have been effectively identified. The trained net model is tampered with since the algorithms' learning is not safe. Therefore, a fully homomorphic deep learning algorithm has been developed to detect vulnerabilities in smart contract systems for blockchain in order to safeguard user data. Initially, user data is stored on the blockchain based on a consensus algorithm that evaluates the operations of each node using a reputation model. Reputation-based Byzantine Fault Tolerance (RBFT) enhances security by assessing users' reputations to prevent malicious behaviour and ensure fault tolerance. Reputation values, ranging from 0 to 1, are crucial for establishing trust and reliability in the network. To further optimize RBFT performance, the Secretary Bird Optimization Algorithm is employed. Smart contract data is derived from source code, including the control flow graph and operation code. XLNet and Bi-LSTM are used to extract features from the control flow graph and operation code, which are then trained and tested using ElGamal cryptography with a Deep Belief Network to improve vulnerability detection and enhance security in blockchain-based smart contract systems. The proposed approach provides 98.40% accuracy, 95.40% positive predictive value (PPV), and 98.80% selectivity. This proposed approach enhances blockchain-based smart contract systems by improving vulnerability detection and ensuring robust encryption of sensitive data through advanced reputation models and cryptographic techniques.</p>