Using platforms like AWS, Azure, or google cloud, which offer flexibility and resilience but also present difficult security concerns, multi-cloud and hybrid clouds methods have become more popular in past years. These settings diversity leads to fragmented security rules, jumbled IAM and overlooked perimeter defenses. Because of the plurality in logging, this fragmented environment makes it harder to conduct effective audits and keeps increasing the risk of insider assaults. We address this gap by introducing a four layered paradigm that integrates blockchain, Artificial intelligence and ZT. The intelligence hub, the security control and AI layer, gathers telemetry from every cloud. Through the application of several AI/ML models, such as LSTM autoencoders and isolation Forest, the time anomaly alert greatly aids in the real time ZT policy decisions by continuously monitoring user behavior and workload variations. The blockchain integrity layer which uses a permission blockchain to ensure non-repudiation and compliance, empirically tests the frameworks theoretical soundness in an emulated hybrid-cloud experiment using Ai logs to measure and check the routing of extracts through model for Ai-based undesired event detection, latency for blockchain-based logging, and correct enforcement in the zero Trust model. To solve the omnipresent security, visibility, and compliance concerns in the fluid multi-cloud and hybrid cloud environments, we introduce in this study for the first time an integrated security methodology that we call the Ai-blockchain-zero trust (ABZT) architecture. Legacy perimeter-centric models can't keep up with advanced persistent threats, (APT), and insider risks; it's time for a new approach: continuous verification (Zero Trust) coupled with state-of-the-art, behavior-based threat analysis (AI) and tamper-proof audit trails (Blockchain).