Recent research in Ant Colony Optimization (ACO) Algorithm-based Scheduling in Cloud Computing focuses on leveraging bio-inspired intelligence to enhance task scheduling, resource utilization, and energy efficiency in dynamic cloud environments. Advanced ACO variants and hybrid models combining ACO with algorithms such as Whale Optimization, Spider Monkey Optimization, and Particle Swarm Optimization are being developed to minimize makespan, cost, and latency while ensuring balanced workload distribution. These methods adaptively select optimal task-resource mappings through pheromone-guided exploration, offering improved convergence rates and scalability. Current studies also extend ACO frameworks to energy-aware and QoS-driven scheduling, demonstrating superior performance for complex and large-scale cloud applications.