The increased adoption of multi-cloud architecture is enabling organizations to become more agile and scalable, yet has created complex, fragmented security issues. Conventional security monitoring infrastructure has veered into siloed data sources, delayed threat visibility and inadequate scale to ingest high-velocity telemetry relating to an array of cloud environments. To overcome these shortcomings, in this paper, we introduce an AI-based multi-cloud security analytics pipeline for real-time threat detection with streaming telemetry. The proposed framework brings together the security logs, network flows, system events and application-level telemetry across multiple cloud service providers in an integrated streaming pipeline. Sophisticated machine learning and deep learning models such as anomaly detection, supervised classification, and temporal sequence modeling are integrated into the pipeline to pinpoint malicious patterns, zero-day attacks, and policy violations in near real time. The architecture utilizes a pair of stream-processing engines and scalable data ingestion ensuring low-latency analytics, high throughput and fault tolerance. In addition, the system has adaptable learning and automatic response capabilities which allow the system to grow along with emerging threats and always reduce false alarms. Our experimental results with multi-cloud telemetry simulation show that the proposed system achieves higher detection accuracy, shorter response time and better operational efficiency when compared to traditional rule-based and batch-processing security systems. The results demonstrate how streaming analytics powered by AI can be a force-multiplier for improving multi-cloud security posture and offering practical guidance to enterprises that are looking for high-performance, intelligently automated and preemptive cyber defense in slippery, liquid cloud environments.