Research on SLA-based Task Scheduling in Cloud Computing focuses on designing algorithms that optimize resource allocation while ensuring compliance with Service Level Agreements (SLAs). Recent advancements include randomized Particle Swarm Optimization methods that allocate tasks to virtual machines to maximize profit and minimize makespan, hierarchical models for federated cloud environments that maintain SLA adherence across multiple platforms, and machine learning-based load balancing techniques that dynamically adjust resource allocation in real-time. These approaches collectively enhance system efficiency, reduce SLA violations, and improve overall performance, emphasizing the critical role of SLA integration in modern cloud task scheduling frameworks.