This chapter delves into the critical role cloud computing plays in enhancing the scalability, storage management, and resource allocation for large-scale video streaming services. As demand for high-quality streaming continues to grow, traditional infrastructure models struggle to manage the bandwidth, storage, and computational resources required to deliver a seamless user experience. Cloud computing offers a flexible and scalable solution to these challenges, enabling streaming platforms to dynamically allocate resources based on demand. This chapter explores machine learning-based predictive storage management techniques using ARIMA and LSTM models, which are used to forecast storage needs based on historical usage patterns and real-time data. Additionally, it discusses cloud resource allocation strategies for managing bandwidth, CPU, and storage resources in an optimized manner. Finally, the chapter analyzes the cost-efficiency trade-offs of cloud-enhanced streaming, including considerations around operational costs, quality of service (QoS), and performance optimization. Through a combination of theoretical analysis, case studies, and real-world examples, this chapter provides insights into how cloud computing can transform the video streaming landscape by optimizing storage and resource management at scale.