<p>Cloud computing has transformed business operations by providing scalable, cost-effective access to computing resources. However, efficiently managing dynamic workloads remains a complex challenge. Accurate workload forecasting is critical to avoid underprovisioning, which leads to poor performance, and overprovisioning, which results in unnecessary costs. Inaccurate predictions can cause inefficient resource allocation, particularly for companies with fluctuating demand. To address these issues, we propose a cooperative learning-enabled neural network model for workload forecasting in cloud environments. The model decomposes workload traces into distinct components, allowing the neural network to learn patterns from each, while cooperative learning ensures no loss of crucial information during optimization. Extensive testing using real-world Google workload traces demonstrates that our model achieves up to 92% higher accuracy compared to state-of-the-art methods. A detailed statistical analysis further validates these performance improvements, confirming the effectiveness of the proposed model in dynamic cloud environments.</p>