<p>This paper gives a novel method for smart contract vulnerability detection (SCVD), titled SCVD utilizing Finite Element Interpolated Neural Networks for Enhanced Security in Blockchain Applications (SCV-BC-FEINN). The proposed method uniquely integrates deep learning and blockchain to develop the reliability and security of smart contract analysis. Initially, the data is fed into the Signed Cumulative Distribution Transform (SCDT) for feature extraction. SCDT extracts opcode-level features such as compute, compare, memory, and stack operations. These extracted features are then passed to the FEINN model to detect vulnerabilities in smart contracts and classify them as secure and vulnerable. The securely classified data is subsequently processed through a blockchain-based storage system integrated with the Proof-of-Monitoring (PoM) consensus mechanism, which makes sure tamper-proof, immutable, and traceable recording of securely classified smart contracts. The proposed SCV-BC-FEINN method is implemented in Python. The proposed approach attained 99.16% precision, 99.34% accuracy, 98.99% Recall, 98.99% F1-score, when analysed to existing techniques like An integrated smart contract vulnerability detection tool utilizing multi-layer perceptron on real-time solidity smart contracts (SCVD-MLP), Fine-grained smart contract vulnerability detection by heterogeneous code feature learning and automated dataset construction (SCVD-GNN), Efficient vulnerability detection in ethereum smart contracts by multimodal learning with graph neural network and language model (VD-ESC-BiLSTM), respectively.</p>