Cloud computing has become a cornerstone of modern IT infrastructure, offering scalable and on-demand resources for diverse applications. However, efficient resource management remains a critical challenge due to dynamic workloads, varying task demands, and energy consumption concerns. This paper proposes a Real-Time Adaptive Task Scheduling Algorithm (RTATS) designed for cloud environments, tackling these challenges through a dynamic, cost-based task allocation strategy. RTATS optimizes scheduling by balancing execution time and energy consumption, leveraging advanced techniques such as Dynamic Voltage and Frequency Scaling (DVFS), dynamic virtual machine activation and deactivation, and real-time load monitoring. It also incorporates a task reallocation mechanism to ensure load balancing and fairness. Extensive simulations demonstrate that RTATS significantly reduces makespan, enhances load balancing, and minimizes energy consumption compared to traditional approaches such as EMA and TRETA. These results validate RTATS as a robust, scalable, and energy-efficient solution for modern cloud environments, with practical applications in enterprise clouds, IoT, and edge computing.