Effective task scheduling in cloud computing is crucial for optimizing system performance and resource utilization. Traditional scheduling methods often struggle to adapt to the dynamic and complex nature of cloud environments, where workloads, resource availability, and task requirements constantly change. Swarm intelligence-based optimization algorithms, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), offer a promising solution by mimicking natural processes to explore large search spaces efficiently. These algorithms are effective in balancing multiple objectives, including minimizing execution time, reducing energy consumption, and ensuring fairness in resource allocation. They also enhance system scalability, which is vital for modern cloud infrastructures. However, challenges remain, including slow convergence speeds, complex parameter tuning, and integration with existing cloud frameworks. Addressing these issues will be essential for the practical implementation of swarm intelligence in cloud task scheduling, helping to improve resource management and overall system performance.