Distributed Denial-of-Service (DDoS) attacks continue to be a constant menace to the availability of online services, necessitating adaptive and intelligent defense mechanisms. This paper presents a novel DDoS prevention system: NeuroGuard, an Autonomous AI Defense Network (AADN) based on deep learning, federated learning, and blockchain. Our framework employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for real-time traffic analysis to provide improved detection and classification of malicious behavior, aided by recent research [11–14]. For data privacy issues, federated learning facilitates training models in a privacy-friendly way among nodes in a distributed manner, discouraging the threats associated with centralized data collection and being part of distributed threat intelligence [6, 9, 10]. Blockchain technology offers a secure and decentralized environment for improving overall system security and trust [7, 8]. Combined with the application of blockchain to log traffic management decisions, produces a tamper-proof audit trail, guaranteeing the integrity and verifiability of mitigation measures taken against DDoS attacks [6]. This guarantees that only authorized and authenticated nodes are allowed to engage in the federated learning process and contribute to model updates, eliminating the threat of malicious node infiltration. This NeuroGuard architecture surpasses traditional DDoS mitigation techniques [1, 5] by offering increased adaptability and scalability. Our initial trials show a drastic improvement in detection rates and reaction time as well as protecting data privacy, allowing for attainment of more stable and secured world-wide network infrastructure. Through deployment of existing strategy in conjunction with AI and blockchain [2–6], NeuroGuard implements cooperative and secured protection against arising DDoS threats, beyond prevalent limitations within present defense tactics, enhancing general security position within linked networks. The system outlined is aimed at establishing an architecture that not only protects against the prevailing DDoS threats but is also secure against upcoming cyber attacks.