Service Level Agreements (SLAs) are essential for defining performance and compliance expectations in cloud computing ecosystems. However, traditional SLA models often lack adaptability, failing to address dynamic workloads, evolving regulatory demands, and rising cybersecurity threats. This study presents an adaptive SLA governance architecture driven by Machine Learning (ML), enabling predictive violation detection, real-time compliance monitoring, and dynamic policy adjustments. The system integrates legal frameworks such as GDPR, ISO/IEC 27001, NIST, and India’s DPDPA, embedding enforceable standards into SLA logic. Our framework employs ML techniques including violation forecasting, unsupervised anomaly detection, privacy risk quantification, and NLP-based clause synthesis, creating self-adjusting, legally resilient SLAs. In simulations with cloud workload datasets, the system achieved 94% prediction accuracy for SLA breaches, 98% anomaly detection, and over 91% success in automated clause alignment. While promising, broader testing in real-world deployments is needed to validate generalizability. This framework bridges regulatory obligations and dynamic service delivery through explainable, transparent SLA orchestration.