Abstract

The rapid adoption of cloud computing has transformed the way organizations deploy, manage, and scale their applications. Enterprises increasingly rely on Multi-Cloud and Hybrid Cloud environments to avoid vendor lock-in, enhance reliability, improve performance, and ensure business continuity. However, managing workloads across multiple cloud providers introduces significant challenges related to workload placement, operational cost optimization, resource utilization, and Service Level Agreement (SLA) compliance. Traditional cloud management approaches depend heavily on static rules and manual intervention, which are often inadequate for dynamic cloud environments. Artificial Intelligence (AI) and Machine Learning (ML) provide opportunities to automate workload placement decisions by analyzing historical workload patterns, predicting resource demands, and continuously optimizing cloud resource allocation. This research proposes an AI-driven framework that integrates machine learning algorithms, predictive analytics, and optimization techniques to intelligently place workloads across Multi-Cloud and Hybrid Cloud infrastructures. The framework considers parameters such as computational requirements, latency, network performance, operational cost, energy consumption, and SLA constraints. The proposed model dynamically predicts workload behavior and selects the optimal cloud environment for deployment. Experimental evaluation demonstrates that AI-based workload placement significantly improves resource utilization, reduces cloud expenditure, minimizes SLA violations, and enhances application performance. The study concludes that intelligent automation can serve as an effective solution for managing increasingly complex cloud ecosystems.