This paper presents a theoretical analysis of a blockchain-based Intrusion Detection System (IDS) designed to improve cybersecurity through decentralized threat intelligence sharing. The proposed IDS framework integrates the Isolation Forest algorithm, a machine learning model, with blockchain technology to enable anomaly detection and automated threat response across a network. Using the Isolation Forest algorithm, the system effectively identifies anomalies in network traffic, while smart contracts on the blockchain allow for autonomous actions, such as node quarantine and malicious IP blocking. This approach aims to reduce the frequency of false positives and enhance response times, providing an efficient solution for scalable and collaborative cybersecurity applications. By examining the potential of combining machine learning with blockchain and smart contracts, this paper contributes to the advancement of adaptive IDS solutions that leverage decentralized architectures for enhanced threat detection and rapid response.