The exponential growth of cloud computing infrastructure has fundamentally transformed the operational and strategic paradigms of modern information systems. This research explores the integration of intelligent machine learning (ML) frameworks within hybrid cloud architectures to optimize workload placement while simultaneously minimizing cost and ensuring stringent service-level agreement (SLA) compliance. The hybrid cloud model, encompassing private and public cloud resources, offers unparalleled flexibility and scalability, yet its operational complexity imposes significant challenges in resource allocation, workload balancing, and energy efficiency (Khan & Zakarya, 2021; Dittakavi, 2023). Recent advances in ML-based predictive modeling enable dynamic, context-aware decision-making that optimizes resource utilization while mitigating performance degradation, thereby addressing critical economic and operational concerns (Hebbar & Maheshkar, 2026). This study critically examines existing literature on ML-driven cloud management strategies, serverless computing paradigms, thermal-aware resource allocation, and adaptive workload prediction models. Methodologically, the research employs a synthesis of theoretical frameworks, simulation-based analyses, and comparative evaluation of heuristic and learning-based approaches. Results underscore the efficacy of ML-enabled hybrid cloud orchestration in achieving measurable reductions in operational costs and energy consumption without compromising SLA adherence (Shahane, 2021; MirhoseiniNejad et al., 2021). The discussion delves into the intricate interplay between workload prediction accuracy, ML model complexity, and cloud heterogeneity, offering a nuanced perspective on scalability, sustainability, and long-term operational efficiency (Kumar & Singh, 2020; Sathupadi, 2023). The research further identifies critical knowledge gaps, particularly in real-time adaptation of ML models under fluctuating workload patterns and multi-cloud scenarios, highlighting opportunities for future empirical validation and cross-domain integration. This study ultimately contributes a comprehensive framework for implementing intelligent workload placement strategies, advancing both theoretical understanding and practical deployment of cost-efficient, SLA-compliant hybrid cloud systems.