This chapter explores the critical role of task scheduling in cloud computing environments, examining various algorithms designed to optimize resource allocation and system performance. It presents an overview of both static and dynamic scheduling approaches, highlighting their respective strengths and limitations in addressing the complex, multifaceted challenges of cloud task management. The discussion encompasses traditional methods such as First Come First Serve (FCFS) and Shortest Job First (SJF), as well as more advanced techniques including Round Robin, Adaptive Scheduling, and heuristic-based algorithms like Genetic Algorithms and Ant Colony Optimization. The chapter analyzes these algorithms in terms of their impact on key performance metrics such as execution time, resource utilization, and load balancing. Additionally, it considers the growing importance of adaptability and multi-objective optimization in response to the dynamic nature of cloud workloads. By comparing different scheduling strategies and their applications, this chapter provides insights into selecting and implementing effective task scheduling solutions for diverse cloud computing scenarios.