The growing complexity in cybersecurity threats has been accelerated by the rapid development of network architectures enabled by the growth of cloud computing, IoT deployments, and edge networks. Conventional reactive security models are insufficient for dealing with sophisticated cyberattacks like zero-day exploits, ransomware, and advanced persistent threats (APTs). This paper presents AI-driven predictive analysis coupled with blockchain technology for network administration and autonomous cybersecurity. Employing machine learning (ML) and deep learning (DL) models, predictive analytics allows for proactive threat detection by analyzing patterns, anomalies, and behavioral deviations as they occur. AI-driven models here enable automatic response to incidents, reducing the incidence of breaches. It serves as a valuable addition to the predictive analytics as blockchain technology safeguards the integrity, transparency, and security of all network operations. Its distributed ledger format maintains tamper-proof recording of cybersecurity incidents, enhancing traceability and confidence in network defense mechanisms. Smart contracts facilitate the deployment of self-executing security policies that dynamically modify network settings in reaction to identified threats. This allows for greater transparency and traceability when integrating various data components into the overall network and also improves mechanisms for authentication and access control, as blockchain-based identities can greatly reduce the risks of credential compromise and unauthorized access. An integrated AI-blockchain cybersecurity framework that improves network resilience with automated threat intelligence, anomaly detection, and real-time attack mitigation was presented. Organizations can create a self-healing cybersecurity ecosystem by harnessing the predictive capabilities of AI and the immutable security of blockchain. Federated learning builds on this concept by enabling distributed AI models that can collaborate securely across multi-cloud environments, while never exposing sensitive data. Such a strategy decreases the probability of the presence of existing threats and has the capacity to amend with current cyber threats.