Cloud computing refers to the ability to obtain and use computer services via the Internet. It offers less expensive capital costs, shared resources, widespread network access, reliability, scalability, and performance. This study focuses on understanding how cloud systems handle scalability, as it is one of the obstacles to cloud providers, by conducting an extensive review of the existing literature on cloud computing and synthesizing all of it to gather information about the scalability problems that affect cloud computing performance. In addition, it assesses the scalability of cloud environments under various workloads. Based on the findings of the literature study, this paper identified cloud computing concerns, such as managing traffic, data growth, big data processing, automated scaling control, and unpredictable cloud applications. As data volumes increase, traditional processing techniques struggle to handle massive amounts of data, affecting scalability and dependability. Performance problems like latency, CPU usage, and memory consumption exacerbate these issues. Furthermore, a solution to address the problems is synthesized by implementing load balancing, which distributes incoming traffic among available resources, reducing bottlenecks and improving system responsiveness. Autoscaling, which adjusts resource allocation based on demand fluctuations, offers a more adaptive and cost-efficient solution, particularly in cloud-based systems, by ensuring resources are optimized to meet varying workloads.