Recent research in Ant Colony Optimization (ACO)-based Workflow Scheduling in Cloud Computing focuses on improving task mapping efficiency, load balancing, and resource utilization through bio-inspired optimization. Modified and hybrid ACO algorithms are being developed to minimize makespan, cost, and energy consumption while effectively handling task dependencies within complex workflows. Studies integrate ACO with other meta-heuristics like Genetic Algorithms, Particle Swarm Optimization, and Spider Monkey Optimization to enhance convergence speed and global search capability. These approaches demonstrate superior adaptability and scalability in heterogeneous cloud environments, enabling intelligent and dynamic workflow scheduling that balances performance and computational overhead.