Recent research in Workflow Scheduling in Cloud Computing emphasizes intelligent, cost-effective, and deadline-aware task management strategies to improve efficiency and resource utilization in large-scale cloud environments. Studies from 2024 and 2025 explore advanced optimization and AI-based methods such as Deep Q-Networks, Whale Optimization Algorithms, and Graph Attention Networks to handle complex scientific and microservice workflows represented as directed acyclic graphs (DAGs). These methods aim to minimize execution time, energy consumption, and operational costs while meeting user-defined QoS and reliability constraints. Additionally, emerging works focus on dynamic and adaptive scheduling mechanisms that integrate multi-objective optimization, containerization, and real-time resource allocation, contributing to more scalable, energy-efficient, and intelligent workflow management in heterogeneous cloud systems.