The rapid adoption of microservices architecture has introduced significant benefits in terms of scalability, flexibility, and modularity. However, it has also created new cybersecurity challenges due to the distributed nature of microservices systems. Traditional security mechanisms are often insufficient in addressing the complex and dynamic threat landscape of modern distributed applications. This research presents a novel cybersecurity framework that combines Artificial Intelligence (AI), Blockchain, and Zero-Trust architecture to enhance the resilience of microservices systems. By leveraging blockchain's decentralized consensus mechanism, the framework ensures tamper-proof security policies, while AI-driven intrusion detection enhances real-time detection and prevention of malicious behaviors. Additionally, the integration of Zero-Trust principles guarantees continuous authentication, least-privilege access, and continuous verification of service interactions. This paper explores the potential of these technologies to collaboratively detect, mitigate, and prevent intrusions dynamically, offering a comprehensive, secure, and adaptive solution for microservices-based systems. The proposed model’s effectiveness is evaluated through performance testing, comparing its capabilities to traditional security models. Results indicate that the integrated approach significantly improves intrusion detection, reduces attack surfaces, and enhances overall system resilience. This framework offers significant implications for securing microservices environments across industries such as cloud computing, finance, and healthcare.